library(factoextra)
package ‘factoextra’ was built under R version 3.6.2Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
#path to protected dbGap data location
local_path <- "~/Documents/MOMS-PI"# change me
#local_path <- "~/MOMS-PI"# change me
path2 <- file.path(local_path,"71694", "PhenoGenotypeFiles", "RootStudyConsentSet_phs001523.MOMS_PI.v1.p1.c1.DS-PREG-COM-IRB-PUB-MDS", "PhenotypeFiles")
otu <- read.csv(file = file.path(path2, "MergedFiles", "OTU_table.csv"))
rownames(otu) <- otu$X
otu$X <- NULL
t_otu <- as.data.frame(t(otu))
meta <- read.csv(file = file.path(path2, "MergedFiles", "Meta_data.csv"))
#physical activity variable
phys <- meta[,98:100]
meta$phys <- ifelse(meta$physical_activity_vigorous == "0_times" | is.na(meta$physical_activity_vigorous), "None", "Vigorous")
meta$phys[which(meta$phys == "None" & meta$physical_activity_moderate != "0_times" & !is.na(meta$physical_activity_moderate))] <- "Moderate"
meta$phys[which(meta$phys == "None" & meta$physical_activity_light != "0_times" & !is.na(meta$physical_activity_light))] <- "Light"
meta$phys <- as.factor(meta$phys)
#filter to just two races
meta <- meta %>%
filter(race == "african_american" | race == "caucasian")
meta$race <- factor(meta$race)
rownames(meta) <- meta$X
meta$X <- NULL
t_otu <- t_otu[rownames(meta),]
meta <- meta %>%
mutate(antibiotics_current = replace_na(antibiotics_current, "No"))
meta <- meta %>%
mutate(infertility_treatment = replace_na(infertility_treatment, "No")) %>%
mutate(infertility_treatment = recode(infertility_treatment, Not_sure = "No", .default = levels(meta$infertility_treatment)))
meta <- meta %>%
mutate(vdischarge = replace_na(vdischarge, "No")) %>%
mutate(vdischarge = recode(vdischarge, Not_Sure = "No", .default = levels(meta$vdischarge)))
meta <- meta %>%
mutate(vodor = replace_na(vodor, "No")) %>%
mutate(vodor = recode(vodor, Not_Sure = "No", .default = levels(meta$vodor)))
meta <- meta %>%
mutate(vitching = replace_na(vitching, "No"))
meta <- meta %>%
mutate(douche = replace_na(douche, "More_than_1_year_ago_or_never"))
meta <- meta %>%
mutate(bv_history = replace_na(bv_history, "No")) %>%
mutate(bv_history = recode(bv_history, Not_Sure = "No", .default = levels(meta$bv_history)))
meta <- meta %>%
mutate(vaginitis_history = replace_na(vaginitis_history, "No"))
meta <- meta %>%
mutate(current_medications = replace_na(current_medications, "No")) %>%
mutate(current_medications = recode(current_medications, No_I_am_not_taking_any_new_medications = "No", .default = levels(meta$current_medications)))
meta <- meta %>%
mutate(smoker_current = replace_na(smoker_current, "No"))
meta <- meta %>%
mutate(alcohol_frequency_year = replace_na(alcohol_frequency_year, "0_times"))
meta <- meta %>%
mutate(diabetes = replace_na(diabetes, "No")) %>%
mutate(diabetes = recode(diabetes, Not_sure = "No", .default = levels(meta$diabetes)))
meta <- meta %>%
mutate(no_false_labor = replace_na(no_false_labor, "Yes")) %>%
mutate(no_false_labor = recode(no_false_labor, No_I_have_not_experienced_false_labor = "Yes", .default = levels(meta$no_false_labor)))
meta <- meta %>%
mutate(no_high_bp = replace_na(no_high_bp, "Yes"))
meta <- meta %>%
mutate(no_vbleeding = replace_na(no_vbleeding, "Yes"))
meta <- meta %>%
mutate(progesterone = replace_na(progesterone, "No")) %>%
mutate(progesterone = recode(progesterone, Not_Sure = "No", .default = levels(meta$progesterone)))
levels(meta$age)[1] <- NA
#Create combined table
full_set <- cbind(t_otu, meta)
full_set$alpha_div <- diversity(full_set[,1:length(names(t_otu))], index="shannon")
full_set$num_reads <- rowSums(t_otu)
#Add vagitype
mypropdata <- full_set[,colnames(t_otu)]
mytypes <- apply(mypropdata,1,which.max)
maxprop <- as.numeric(mypropdata[matrix(c(1:nrow(mypropdata),mytypes), ncol=2)])
mytypes <- colnames(mypropdata)[mytypes]
mytypes[maxprop < 0.3] <- "No Type"
mytypes <- as.factor(mytypes)
full_set <- full_set %>%
mutate(vagitype=mytypes)
high_vagitype <- sort(table(full_set$vagitype), decreasing=T)[1:5]
high_vagitype
Lactobacillus_iners Lactobacillus_crispatus_cluster Lachnospiraceae_BVAB1
714 396 228
Gardnerella_vaginalis Lactobacillus_gasseri_cluster
128 64
#Add proportions
full_set$p_Iners <- full_set$Lactobacillus_iners/full_set$num_reads
full_set$p_Crisp <- full_set$Lactobacillus_crispatus_cluster/full_set$num_reads
full_set$p_Gard <- full_set$Gardnerella_vaginalis/full_set$num_reads
full_set$Gass <- full_set$Lactobacillus_gasseri_cluster/full_set$num_reads
full_set$p_BVAB <- full_set$Lachnospiraceae_BVAB1/full_set$num_reads
get_mode = function(x, multimodal.na="TRUE"){
modes <- Mode(x)
if (multimodal.na=="FALSE" | length(modes)==1) {
return(modes[1])
} else {
return(modes[length(modes)+1])
}
}
#582 subjects
#aggregate at patient level
demog <- aggregate(visit_number ~ SUBJECT_ID, data = meta%>%data.frame, FUN = min)
demog <- merge(demog,meta%>%data.frame, by = c("SUBJECT_ID", "visit_number") )
#number visits per subject
df <- aggregate(visit_number ~ SUBJECT_ID, data = meta%>%data.frame, FUN = length)
colnames(df)[which(colnames(df) == "visit_number")] <- "number of visits"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average income per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(income = get_mode(income, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "income")] <- "average income"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average education per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(education = get_mode(education, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "education")] <- "average education"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average age per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(age = get_mode(age, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "age")] <- "average age"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average prenatal care start per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(prenatal_care_start = get_mode(prenatal_care_start, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "prenatal_care_start")] <- "average p_care_start"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average infertility treatment per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(infertility_treatment = get_mode(infertility_treatment, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "infertility_treatment")] <- "average inf_trt"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average v_discharge per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vdischarge = get_mode(vdischarge, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vdischarge")] <- "average v_discharge"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average v_odor per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vodor = get_mode(vodor, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vodor")] <- "average v_odor"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average v_itching per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vitching = get_mode(vitching, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vitching")] <- "average v_itching"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average douche per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(douche = get_mode(douche, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "douche")] <- "average douche"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average bv_history per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(bv_history = get_mode(bv_history, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "bv_history")] <- "average bv_history"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average vaginitis_history per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vaginitis_history = get_mode(vaginitis_history, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vaginitis_history")] <- "average vaginitis_history"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average current_medications per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(current_medications = get_mode(current_medications, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "current_medications")] <- "average current_medications"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average smoker_current per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(smoker_current = get_mode(smoker_current, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "smoker_current")] <- "average smoker_current"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average alcohol_frequency per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(alcohol_frequency_year = get_mode(alcohol_frequency_year, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "alcohol_frequency_year")] <- "average alcohol_frequency"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average diabetes per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(diabetes = get_mode(diabetes, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "diabetes")] <- "average diabetes"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average false_labor per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(no_false_labor = get_mode(no_false_labor, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "no_false_labor")] <- "average no_false_labor"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average high_bp per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(no_high_bp = get_mode(no_high_bp, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "no_high_bp")] <- "average no_high_bp"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average v_bleeding per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(no_vbleeding = get_mode(no_vbleeding, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "no_vbleeding")] <- "average no_vbleeding"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average progesterone per subject
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(progesterone = get_mode(progesterone, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "progesterone")] <- "average progesterone"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#average vaginal_ph across visits
#Some patients have na so goes to 568
df <- aggregate(vaginal_pH ~ SUBJECT_ID, data = meta%>%data.frame, FUN = mean)
colnames(df)[which(colnames(df) == "vaginal_pH")] <- "mean_vaginal_ph"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#alpha diversity
df <- aggregate(alpha_div ~ SUBJECT_ID, data = full_set%>%data.frame, FUN = mean)
colnames(df)[which(colnames(df) == "alpha_div")] <- "mean_alpha_div"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#reported_ga is only for minimum recorded visit for each individual
Varnames <- c("mean_vaginal_ph", "number of visits", "reported_ga", "mean_alpha_div", "average income",
"average education", "average age", "average p_care_start", "average inf_trt",
"average v_discharge", "average v_odor", "average v_itching", "average douche",
"average bv_history", "average vaginitis_history", "average current_medications",
"average smoker_current", "average alcohol_frequency", "average diabetes",
"average no_false_labor", "average no_high_bp", "average no_vbleeding", "average progesterone")
#racial breakdown, break down visits and other demographics
stratified1 = tableone::CreateTableOne(
vars = Varnames[1:4],
data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
stratified2 = tableone::CreateTableOne(
vars = Varnames[5:7],
data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
stratified3 = tableone::CreateTableOne(
vars = Varnames[8:15],
data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
The data frame does not have: average v_discharge Dropped
stratified4 = tableone::CreateTableOne(
vars = Varnames[16:19],
data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
stratified5 = tableone::CreateTableOne(
vars = Varnames[20:23],
data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
# knitr::kable(format="latex")%>%
# kable_styling("striped", full_width = F, font_size = 9)
stratified1
Stratified by race
african_american caucasian p test
n 366 152
mean_vaginal_ph (mean (SD)) 4.85 (0.80) 4.74 (0.72) 0.130
number of visits (mean (SD)) 3.57 (1.72) 3.36 (1.49) 0.196
reported_ga (%) 0.032
First Trimester 86 (23.5) 48 (31.6)
Second Trimester 179 (48.9) 76 (50.0)
Third Trimester 98 (26.8) 25 (16.4)
NA 3 ( 0.8) 3 ( 2.0)
mean_alpha_div (mean (SD)) 1.12 (0.59) 0.91 (0.65) <0.001
#fix the part where every race is shown, probably due to meta being called
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
# knitr::kable(format="latex")%>%
# kable_styling("striped", full_width = F, font_size = 9)
stratified2
Stratified by race
african_american caucasian p test
n 366 152
average income (%) <0.001
15_000_19_999 33 ( 9.0) 9 ( 5.9)
20_000_39_999 40 (10.9) 20 (13.2)
40_000_59_999 8 ( 2.2) 30 (19.7)
60_000_79_999 3 ( 0.8) 25 (16.4)
80_000_or_more 1 ( 0.3) 37 (24.3)
Under_15_000 267 (73.0) 27 (17.8)
NA 14 ( 3.8) 4 ( 2.6)
average education (%) <0.001
2_year_College_Degree 29 ( 7.9) 16 (10.5)
4_year_College_Degree 7 ( 1.9) 47 (30.9)
Doctoral_or_Professional_Degree 1 ( 0.3) 18 (11.8)
High_School_GED 182 (49.7) 27 (17.8)
Less_than_High_School 50 (13.7) 1 ( 0.7)
Masters_Degree 2 ( 0.5) 19 (12.5)
Some_College 91 (24.9) 24 (15.8)
NA 4 ( 1.1) 0 ( 0.0)
average age (%) <0.001
0 ( 0.0) 1 ( 0.7)
18 7 ( 1.9) 1 ( 0.7)
18_to_28 239 (65.3) 49 (32.2)
29-38 97 (26.5) 91 (59.9)
Above_38 15 ( 4.1) 9 ( 5.9)
Below_18 6 ( 1.6) 1 ( 0.7)
NA 2 ( 0.5) 0 ( 0.0)
#fix the part where every race is shown, probably due to meta being called
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
# knitr::kable(format="latex")%>%
# kable_styling("striped", full_width = F, font_size = 9)
stratified3
Stratified by race
african_american caucasian p test
n 366 152
average p_care_start (%) <0.001
0_4_weeks_after_conception 101 (27.6) 33 (21.7)
4_weeks_to_the_end_of_the_first_trimester 157 (42.9) 88 (57.9)
Before_conception 9 ( 2.5) 11 ( 7.2)
During_the_second_trimester 51 (13.9) 14 ( 9.2)
During_the_third_trimester 7 ( 1.9) 2 ( 1.3)
I_did_not_receive_prenatal_care 15 ( 4.1) 0 ( 0.0)
I_have_not_yet_received_prenatal_care_but_planning_to 18 ( 4.9) 1 ( 0.7)
NA 8 ( 2.2) 3 ( 2.0)
average inf_trt = Yes (%) 5 ( 1.4) 10 ( 6.6) 0.003
average v_odor = Yes (%) 30 ( 8.2) 4 ( 2.6) 0.033
average v_itching = Yes (%) 23 ( 6.3) 5 ( 3.3) 0.246
average douche (%) <0.001
More_than_1_month_ago 84 (23.0) 8 ( 5.3)
More_than_1_year_ago_or_never 279 (76.2) 144 (94.7)
Within_the_last_24_hours 3 ( 0.8) 0 ( 0.0)
average bv_history (%) 0.494
No 286 (78.1) 125 (82.2)
Yes 79 (21.6) 27 (17.8)
NA 1 ( 0.3) 0 ( 0.0)
average vaginitis_history = Yes (%) 13 ( 3.6) 2 ( 1.3) 0.274
#fix the part where every race is shown, probably due to meta being called
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
# knitr::kable(format="latex")%>%
# kable_styling("striped", full_width = F, font_size = 9)
stratified4
Stratified by race
african_american caucasian p test
n 366 152
average current_medications = Yes (%) 141 (38.5) 99 (65.1) <0.001
average smoker_current = Yes (%) 54 (14.8) 21 (13.8) 0.889
average alcohol_frequency (%) <0.001
0_times 223 (60.9) 36 (23.7)
1_2_times 54 (14.8) 28 (18.4)
10_19_times 12 ( 3.3) 20 (13.2)
20_times 13 ( 3.6) 37 (24.3)
3_5_times 42 (11.5) 19 (12.5)
6_9_times 21 ( 5.7) 12 ( 7.9)
NA 1 ( 0.3) 0 ( 0.0)
average diabetes (%) 0.529
No 356 (97.3) 145 (95.4)
Yes 9 ( 2.5) 6 ( 3.9)
NA 1 ( 0.3) 1 ( 0.7)
#fix the part where every race is shown, probably due to meta being called
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
# knitr::kable(format="latex")%>%
# kable_styling("striped", full_width = F, font_size = 9)
stratified5
Stratified by race
african_american caucasian p test
n 366 152
average no_false_labor (%) NaN
0 ( 0.0) 0 ( 0.0)
No 37 (10.1) 2 ( 1.3)
Yes 321 (87.7) 148 (97.4)
NA 8 ( 2.2) 2 ( 1.3)
average no_high_bp (%) NaN
0 ( 0.0) 0 ( 0.0)
No 42 (11.5) 9 ( 5.9)
Yes 312 (85.2) 140 (92.1)
NA 12 ( 3.3) 3 ( 2.0)
average no_vbleeding (%) 0.057
0 ( 0.0) 3 ( 2.0)
No 31 ( 8.5) 14 ( 9.2)
Yes 327 (89.3) 131 (86.2)
NA 8 ( 2.2) 4 ( 2.6)
average progesterone (%) 0.520
No 358 (97.8) 147 (96.7)
Yes 7 ( 1.9) 5 ( 3.3)
NA 1 ( 0.3) 0 ( 0.0)
#fix the part where every race is shown, probably due to meta being called
#Find out individuals who have a different vagitype across visits
uniq_vagitype <- full_set %>%
group_by(SUBJECT_ID) %>%
summarize(n_unique = n_distinct(vagitype))
more_vagitype <- uniq_vagitype %>%
filter(n_unique > 1)
full_set %>%
filter(SUBJECT_ID %in% more_vagitype$SUBJECT_ID) %>%
group_by(SUBJECT_ID) %>%
distinct(vagitype)
#Look at proportional changes over time, look at differences betweeen those that did change and those that did not
#Look at how they change, if they change similarly
count_table <- aggregate(list(full_set$Lactobacillus_iners, full_set$p_Iners,full_set$Lactobacillus_crispatus_cluster, full_set$p_Crisp, full_set$Gardnerella_vaginalis, full_set$p_Gard, full_set$Lactobacillus_gasseri_cluster, full_set$Gass, full_set$Lachnospiraceae_BVAB1, full_set$p_BVAB), by = list(full_set$race, full_set$reported_ga), mean)
colnames(count_table) <- c("race", "reported_ga", "Lactobacillus_iners", "p_Iners", "Crispatus", "p_Crisp",
"Gardnerella", "p_Gard", "Gasseri", "p_Gass", "BVAB", "p_BVAB")
count_table <- count_table %>%
arrange(race)
kable(count_table, table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"))
| race |
reported_ga |
Lactobacillus_iners |
p_Iners |
Crispatus |
p_Crisp |
Gardnerella |
p_Gard |
Gasseri |
p_Gass |
BVAB |
p_BVAB |
| african_american |
First Trimester |
14505.96 |
0.2517823 |
8966.637 |
0.1340359 |
7985.951 |
0.1107511 |
652.6078 |
0.0149517 |
8863.471 |
0.1252649 |
| african_american |
Second Trimester |
21640.13 |
0.3400013 |
10559.085 |
0.1383411 |
5455.281 |
0.0905662 |
867.7945 |
0.0155340 |
10614.083 |
0.1433958 |
| african_american |
Third Trimester |
26061.41 |
0.4024149 |
10693.302 |
0.1511822 |
3437.836 |
0.0607404 |
1055.6137 |
0.0177334 |
7438.261 |
0.1027035 |
| caucasian |
First Trimester |
12899.98 |
0.1803131 |
24551.900 |
0.3699975 |
9399.640 |
0.1066708 |
2068.4200 |
0.0527995 |
1095.420 |
0.0132448 |
| caucasian |
Second Trimester |
12919.27 |
0.2402719 |
27804.674 |
0.3455106 |
3564.785 |
0.0595546 |
5281.7403 |
0.0920684 |
3087.961 |
0.0345624 |
| caucasian |
Third Trimester |
14819.43 |
0.2555657 |
23575.302 |
0.3017310 |
3401.765 |
0.0562494 |
4193.1306 |
0.0713278 |
1420.843 |
0.0170265 |
#Plot proportions over time, plot count over time
iners <- ggplot(count_table, aes(reported_ga, p_Iners)) +
geom_line(aes(group=race, color=race))
crisp <- ggplot(count_table, aes(reported_ga, p_Crisp)) +
geom_line(aes(group=race, color=race))
gard <- ggplot(count_table, aes(reported_ga, p_Gard)) +
geom_line(aes(group=race, color=race))
gass <- ggplot(count_table, aes(reported_ga, p_Gass)) +
geom_line(aes(group=race, color=race))
bvab <- ggplot(count_table, aes(reported_ga, p_BVAB)) +
geom_line(aes(group=race, color=race))
library(gridExtra)
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combine
ggarrange(iners, crisp, bvab, gard, gass, nrow = 3)
$`1`
$`2`
attr(,"class")
[1] "list" "ggarrange"


#Plot individuals for each race
#ggplot(full_set, aes(reported_ga, p_Iners)) +
# geom_line(aes(group=SUBJECT_ID, color=race)) +
# facet_wrap(~race, ncol=1)
#Look at those with the specific taxa as their vagitype
prop_plot <- ggplot(full_set, aes(p_Iners)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_Iners), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_Iners), lwd=1, linetype=2, color="green")
#prop_plot
cprop_plot <- ggplot(full_set, aes(p_Crisp)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_Crisp), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_Crisp), lwd=1, linetype=2, color="green")
#iprop_plot
bprop_plot <- ggplot(full_set, aes(p_BVAB)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_BVAB), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_BVAB), lwd=1, linetype=2, color="green")
#pprop_plot
gprop_plot <- ggplot(full_set, aes(p_Gard)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_Gard), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_Gard), lwd=1, linetype=2, color="green")
#tprop_plot
gasprop_plot <- ggplot(full_set, aes(Gass)) + geom_histogram() + geom_vline(xintercept=mean(full_set$Gass), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$Gass), lwd=1, linetype=2, color="green")
#bprop_plot
#stratify by race
ggarrange(prop_plot, cprop_plot, bprop_plot, gprop_plot, gasprop_plot, labels = c("Iners","Crisp", "BVAB", "Gard", "Gass"), ncol = 3, nrow = 2)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

race_prop_plot <- ggplot(full_set, aes(p_Iners)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race)
crace_prop_plot <- ggplot(full_set, aes(p_Crisp)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race)
#stratify by race
brace_prop_plot <- ggplot(full_set, aes(p_BVAB)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race)
grace_prop_plot <- ggplot(full_set, aes(p_Gard)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race)
gassrace_prop_plot <- ggplot(full_set, aes(Gass)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race)
ggarrange(race_prop_plot, crace_prop_plot, brace_prop_plot, grace_prop_plot, gassrace_prop_plot, nrow = 3)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
$`1`
$`2`
attr(,"class")
[1] "list" "ggarrange"


#Zero-Inflated Poisson Model
z <- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + (1|SUBJECT_ID), data = full_set, family = poisson, zi = ~ vaginal_pH + reported_ga + alpha_div)
summary(z)
hist(predict(z))
#summary(m1 <- zeroinfl(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div |SUBJECT_ID, data = full_set))
z <- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + (1|SUBJECT_ID), data = full_set, family = poisson, zi = ~ 0)
summary(z)
hist(predict(z))
#Zero-Inflated Negative Binomial Model
neg_bin<- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + offset(log(num_reads)) + (1|SUBJECT_ID), data = full_set, family = nbinom2, zi = ~ vaginal_pH + reported_ga + alpha_div)
summary(neg_bin)
predict(neg_bin)
neg_binone <- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + offset(log(num_reads)) + (1|SUBJECT_ID), data = full_set, family = nbinom2, zi = ~ 1)
summary(neg_binone)
hist(predict(neg_binone))
#Zero-Inflated Beta-Binomial Model
#kostic.y <- cbind(1, kostic.y=="Tumor")
beta_bin<- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set, family=betabinomial(link = "logit"), zi = ~ vaginal_pH + reported_ga + alpha_div + race)
#Mixed Linear Regression Model
mixedL <- lmer(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set)
summary(mixedL)
linpred <- predict(mixedL)
plot(full_set$Lactobacillus_iners[which(names(linpred) %in% rownames(full_set))], linpred)
#predicted
#x-axis people
#y-axis visits for that person
plot(full_set$Lactobacillus_iners[which(names(linpred) %in% rownames(full_set))], linpred)
## Beta bimonial zero inflated ##
## NBZIMM ##
f21 = glmm.zinb(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div , random = ~ 1|SUBJECT_ID, data = full_set)
summary(f21)
summary(f21)$tTable[4, 5]
hist(full_set$p_Iners)
beta_bin<- glmmTMB(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set, family=betabinomial(link = "logit"), zi = ~ vaginal_pH + reported_ga + alpha_div + race)
mean(full_set$p_Iners)
var(full_set$p_Iners)
library(zoib)
#zoib
#zoib(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race | 1|1, data = full_set, random = 1, EUID = full_set$SUBJECT_ID)
library(aod)
full_set$p_Iners[which(full_set$p_Iners == 1)] <- 0.99999
betaIners <- glmmTMB(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set, family=beta_family(link = "logit"), zi = ~ 1)
betapred <- predict(betaIners)
summary(betaIners)
#predicted
#x-axis people
#y-axis visits for that person
#plot(full_set$p_Iners[which(names(betapred) %in% rownames(full_set))], betapred)
#zoib(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race | 1, data = full_set)
mixedL <- lmer(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set)
#par(mfrow = c(2,2))
coefplot2(mixedL)
plot(mixedL)
linpred <- predict(mixedL)
#predicted
#x-axis people
#y-axis visits for that person
plot(full_set$p_Iners[which(names(linpred) %in% rownames(full_set))], linpred)
full_set$class_Iners <- as.factor(ifelse(full_set$p_Iners > mean(full_set$p_Iners), "High", "Low"))
full_set$class_Crisp <- as.factor(ifelse(full_set$p_Crisp > mean(full_set$p_Crisp), "High", "Low"))
full_set$class_Gass <- as.factor(ifelse(full_set$Gass > mean(full_set$Gass), "High", "Low"))
full_set$class_Gard <- as.factor(ifelse(full_set$p_Gard > mean(full_set$p_Gard), "High", "Low"))
full_set$class_BVAB <- as.factor(ifelse(full_set$p_BVAB > mean(full_set$p_BVAB), "High", "Low"))
#Check median
#add line to show where each point is
#install.packages("ggpubr")
iners_plot <- ggplot(full_set, aes(class_Iners)) + geom_bar()
snea_plot <- ggplot(full_set, aes(class_Crisp)) + geom_bar()
tm_plot <- ggplot(full_set, aes(class_Gass)) + geom_bar()
prev_plot <- ggplot(full_set, aes(class_Gard)) + geom_bar()
bvab_plot <- ggplot(full_set, aes(class_BVAB)) + geom_bar()
ggarrange(iners_plot, snea_plot, tm_plot, prev_plot, bvab_plot, labels = c("Iners", "Crisp", "Gass", "Gard", "BVAB1"), ncol = 3, nrow = 2)
#Distribution of High and Low (do it across race)
irace_prop_plot <- ggplot(full_set, aes(class_Iners)) + geom_bar(aes(y= stat(prop),group=race, fill=race), position = "dodge2")+ ggtitle("Iners Dichotomized Histogram")
irace_prop_plot

#Distribution of High and Low (do it across race)
srace_prop_plot <- ggplot(full_set, aes(class_Crisp)) + geom_bar(aes(y= stat(prop), group=race, fill=race), position = "dodge2") + ggtitle("Crisp Dichotomized Histogram")
#Distribution of High and Low (do it across race)
prace_prop_plot <- ggplot(full_set, aes(class_Gass)) + geom_bar(aes(y= stat(prop), group=race, fill=race), position = "dodge2") + ggtitle("Gass Dichotomized Histogram")
#Distribution of High and Low (do it across race)
trace_prop_plot <- ggplot(full_set, aes(class_Gard)) + geom_bar(aes(y= stat(prop), group=race, fill=race), position = "dodge2") + ggtitle("Gard Dichotomized Histogram")
#Distribution of High and Low (do it across race)
brace_prop_plot <- ggplot(full_set, aes(class_BVAB)) + geom_bar(aes(y= stat(prop),group=race, fill=race), position = "dodge2") + ggtitle("BVAB Dichotomized Histogram")
ggarrange(irace_prop_plot, srace_prop_plot, prace_prop_plot, trace_prop_plot, brace_prop_plot, nrow = 3)
$`1`
$`2`
attr(,"class")
[1] "list" "ggarrange"


#Use proportions instead of counts for better comparison, DONE
#position dodge, DONE
#Add proportion values to top of bars
#do facet wrap by taxa
covs.taxa <- function(df, taxon, model, vars){
tax_prop <- df[,taxon]/df[,"num_reads"]
if(model == "fixed"){
#covs <- vars
model_data <- df[,vars]
half <- paste(vars, collapse= "+")
}
else{
#covs <- vars
model_data <- df[,c(vars, "SUBJECT_ID", "num_reads")]
half <- paste(paste(vars, collapse= "+"), " + (1|SUBJECT_ID)")
}
model_data <- cbind(tax_prop, model_data)
no.na.data <- na.omit(model_data)
form <- as.formula(paste("tax_prop ~ ", half))
if(model == "fixed"){
mod <- lm(form, data = no.na.data)
step.back <- stepAIC(mod, direction = "backward")
return(step.back)
#return(mod)
}
else{
mod <- lmer(form, data = no.na.data)
return(mod)
#return(mod.step)
}
}
taxa.glm <- function(df, taxon, type, model){
tax_count <- df[,taxon]
tax_prop <- df[,taxon]/df[,"num_reads"]
class_tax <- as.factor(ifelse(tax_prop > mean(tax_prop), "High", "Low"))
class_tax <- recode(class_tax, "High"= 1, "Low"= 0)
if(model == "fixed"){
covs <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
model_data <- df[,covs]
half <- paste(covs, collapse= "+")
}
else{
covs <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
model_data <- df[,c(covs, "SUBJECT_ID", "num_reads")]
half <- paste(paste(covs, collapse= "+"), " + (1|SUBJECT_ID)")
}
if(type == "count"){
model_data <- cbind(tax_count, model_data)
no.na.data <- na.omit(model_data)
form <- as.formula(paste("tax_count ~ ", half))
if(model == "fixed"){
mod <- glm.nb(form, data = no.na.data)
}
else{
mod <- glmer.nb(form, data = no.na.data)
}
}
else if(type == "prop"){
model_data <- cbind(tax_prop, model_data)
no.na.data <- na.omit(model_data)
form <- as.formula(paste("tax_prop ~ ", half))
if(model == "fixed"){
mod <- lm(form, data = no.na.data)
#return(mod)
}
else{
mod <- lmer(form, data = no.na.data)
return(mod)
#return(mod.step)
}
}
else{
model_data <- cbind(class_tax, model_data)
no.na.data <- na.omit(model_data)
form <- as.formula(paste("class_tax ~ ", paste(covs, collapse= "+")))
if(model == "fixed"){
mod <- glm(form, data = no.na.data, family = binomial)
}
else{
mod <- glmer(form, data = no.na.data, family = binomial, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
}
}
#step.model <- stepAIC(fixed_mod, direction = "both", trace = FALSE)
step.back <- stepAIC(mod, direction = "backward", trace = FALSE)
return(step.back)
#TO ADD: Plot for each covariate the predicted distribution
#Output table of common variables, extract p-value
}
model_plot <- function(model){
final_covs <- names(model$model)[-1]
print(deparse(substitute(model)))
print(as.formula(paste("class_tax ~ ", paste(final_covs, collapse= "+"))))
#Plot AIC for model
vars <- unlist(lapply(strsplit(as.character(model$anova$Step), '- ', fixed = TRUE), '[', 2))
vars[1] <- "Intercept"
AIC.res <- data.frame(Variable = vars, AIC = model$anova$AIC)
AIC.res$Variable <- factor(AIC.res$Variable, levels = vars)
#true variables
#true_vars <- paste0("var", 1:10)
#colour_vars <- ifelse(AIC.res$Variable %in% true_vars, 'red','black')
plot <- ggplot(data=AIC.res, aes(x=Variable, y=AIC, group=1)) +
geom_line(color = "red")+
theme_bw()+
theme(axis.text.x = element_text(angle = 60, hjust = 1))
return(plot)
}
pred_plot <- function(df, model){
model_data <- df[,covs]
model_data <- cbind(class_tax, model_data)
no.na.data <- na.omit(model_data)
final_covs <- names(model$model)[-1]
pred_data <- no.na.data[,final_covs]
return(predict(model, newdata=pred_data, type = "response"))
# if(c("alpha_div","vagitype") %in% names(model$model)){
# plotting_dfm <- expand.grid(alpha_div = seq(0, 3.5, 0.01),
# vagitype = c("Lactobacillus_crispatus_cluster", "Lactobacillus_iners", "Lachnospiraceae_BVAB1", "Gardnerella_vaginalis", "Lactobacillus_gasseri_cluster"))
# plotting_dfm$preds <- predict(model, newdata=plotting_dfm, type = "response")
# pl <- ggplot(plotting_dfm, aes(x=alpha_div, y =preds, color=as.factor(vagitype))) +
# geom_point( ) +
# ggtitle("Predicted High/Low by Alpha_Div and Vagitype")
# return(p1)
# }
# else{
# return ("No value")
# }
}
get_data <- function(taxon, df){
tax_prop <- df[,taxon]/df[,"num_reads"]
covs <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
model_data <- df[,c(covs, "SUBJECT_ID", "num_reads")]
model_data <- cbind(tax_prop, model_data)
no.na.data <- na.omit(model_data)
return(no.na.data)
}
make_hists<- function(model, taxon, df, vars){
hists <- data.frame(xx = c(predict(model, newdata = get_data(taxon, df)[, vars]), get_data(taxon, df)[, "tax_prop"]), yy = rep(c("pred", "actual"),each = length(get_data(taxon, df)[, "tax_prop"])))
return(ggplot(hists, aes(x=xx, fill=yy)) + geom_histogram(alpha=0.2, position="identity"))
}
agg_plot <- function(model, taxon, df, vars){
ex <- data.frame(data = c(predict(model, newdata = get_data(taxon, df)[, vars]), get_data(taxon, df)[, "tax_prop"]),
race = get_data(taxon, df)[, "race"], tri = get_data(taxon, df)[, "reported_ga"],
yy = rep(c("pred", "actual"),each = length(get_data(taxon, df)[, "tax_prop"])))
agg_table <- aggregate(list(ex$data), by = list(ex$race, ex$tri, ex$yy), mean)
colnames(agg_table) <- c("race", "tri", "type", "data")
agg_plot <- ggplot(agg_table, aes(tri, data)) +
geom_line(aes(group=interaction(race, type), color=race, linetype = type)) +
scale_x_discrete(labels=c("First Trimester" = "1st", "Second Trimester" = "2nd",
"Third Trimester" = "3rd")) +
ylab("Prop")
return(agg_plot)
}
getPerformace <- function(model, df){
taxa <- colnames(t_otu)
example <- c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster", "Gardnerella_vaginalis", "Lachnospiraceae_BVAB1", "Lactobacillus_gasseri_cluster", "Sneathia_amnii", "Prevotella_cluster2", "TM7_OTU-H1")
for (name in example){
md <- covs.taxa
}
perf <- model_performance(model, metrics = "all", verbose = TRUE)
return(perf)
}
subsetFull <- function(df, taxalist){
`%ni%` <- Negate(`%in%`)
cols <- sapply(taxalist, function (x) any(df[,x]/df$num_reads > 0.05))
df <- subset(df,select = names(df) %ni% names(which(cols == FALSE)))
return(df)
}
covs <- c("vaginal_pH", "alpha_div", "reported_ga", "race", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
model_data <- full_set[,covs]
ggpairs(model_data, columns = 1:2, ggplot2::aes(colour=race)) #too many points, too many vagitypes as well so had to remove
#do top 5 vagitypes
aa <- full_set[full_set$race == 'african_american',]
histogram(aa$vaginal_pH)
count_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "count", "fixed") #Does not converge
prop_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "prop", "fixed")
class_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "class", "fixed")
mcount_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "count", "mixed") #Does not converge
mprop_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "prop", "mixed") #failure to converge in 10000 evaluations
mod_Crisp <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "class", "fixed")
mod_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "class", "fixed")
mod_BVAB <- taxa.glm(full_set, "Lachnospiraceae_BVAB1", "class", "fixed")
mod_Gard <- taxa.glm(full_set, "Gardnerella_vaginalis", "class", "fixed")
mod_Gass <- taxa.glm(full_set, "Lactobacillus_gasseri_cluster", "class", "fixed")
taxalist <- c("Lactobacillus_crispatus_cluster", "Lactobacillus_iners", "Lachnospiraceae_BVAB1", "Gardnerella_vaginalis", "Lactobacillus_gasseri_cluster")
var_df <- data.frame(matrix(ncol = 15, nrow = 15))
#colnames(var_df) <- taxalist
count <- 0
for(ind in c(1:length(taxalist))){
mods <- taxa.glm(full_set, taxalist[ind], "prop", "mixed")
no.na.data <- get_data(taxalist[ind], full_set)
step.back <- lmerTest::step(mods)
fix_vars <- c(rownames(step.back$fixed[which(step.back$fixed$Eliminated == 0),]), rownames(step.back$random[which(step.back$random$Eliminated == 0),]))
for(i in c(1:length(fix_vars))){
var_df[i,ind+count] <- fix_vars[i]
}
fixed_mods <- taxa.glm(full_set, taxalist[ind], "prop", "fixed")
fixed_vars <- names(fixed_mods$model)[-1]
for(i in c(1:length(fixed_vars))){
var_df[i,ind+1+count] <- fixed_vars[i]
}
class_mods <- taxa.glm(full_set, taxalist[ind], "class", "fixed")
class_vars <- names(class_mods$model)[-1]
for(i in c(1:length(class_vars))){
var_df[i,ind+2+count] <- class_vars[i]
}
count <- count + 2
}
colnames(var_df) <- c("Crisp_P_M", "Crisp_P_F", "Crisp_C_F", "Iners_P_M", "Iners_P_F","Iners_C_F", "BVAB1_P_M", "BVAB1_P_F","BVAB1_C_F","Gard_P_M", "Gard_P_F", "Gard_C_F","Gass_P_M","Gass_P_F", "Gass_C_F")
options(knitr.kable.NA = '')
kable(var_df, table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
add_header_above(c("Crispatus" = 3, "Iners" = 3, "BVAB" = 3, "Gardnerella" = 3, "Gasseri" = 3))
#Make table for the variables that are outputted
#need the data frame
#alpha div, vagitype, age, smoking, education, subject_id, vaginal_ph, race
#look at covariate estimates for each taxa model, consider correlations in model
#forward model selection, one variable addition at a time
#look at differences between taxa
#look for vagitypes in rare taxa
#performance with and without vagitype, compare using model comparison,ANOVA DONE
#overlay averages across each race across the 3 trimesters, output model metrics, try without and with vagitype: WROTE FUNCTION, APPLY TO ALL TAXA
#how much does vagitype add in terms of prediction, PLOTS DONE
#make summaries
#pca under both smoothed and unsmoother conditions
#prcomp on raw data, scale variables to same unit
vars <- c("vaginal_pH", "alpha_div", "race", "vagitype", "age", "education", "smoker_current")
mx_vars<- c("vaginal_pH", "alpha_div", "race", "vagitype", "age", "education", "smoker_current", "reported_ga","SUBJECT_ID")
mx_type <- c("vaginal_pH", "alpha_div", "race", "age", "education", "smoker_current", "reported_ga","SUBJECT_ID")
no_type <- c("vaginal_pH", "alpha_div", "race", "age", "education", "smoker_current")
#predict based on these models
taxalist <- c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster", "Gardnerella_vaginalis", "Lachnospiraceae_BVAB1", "Lactobacillus_gasseri_cluster", "Sneathia_amnii", "Prevotella_cluster2", "TM7_OTU-H1")
tax_plots <- list()
tax_hists <- list()
#taxalist <- c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster")
for (taxa in taxalist){
mixed_full <- covs.taxa(full_set, taxa, "mixed", vars)
mixed_notype <- covs.taxa(full_set, taxa, "mixed", no_type)
print(taxa)
print(anova(mixed_full, mixed_notype))
#print(summary(mixed_notype))
var = paste(taxa, "Full", sep = "_")
var2 = paste(taxa, "NoType", sep = "_")
tax_plots[[var]] <- agg_plot(mixed_full, taxa, full_set, mx_vars) + ggtitle(var)
tax_plots[[var2]] <- agg_plot(mixed_notype, taxa, full_set, mx_type) + ggtitle(var2)
tax_hists[[var]] <- make_hists(mixed_full, taxa, full_set, mx_vars) + ggtitle(var)
tax_hists[[var2]] <- make_hists(mixed_notype, taxa, full_set, mx_type) + ggtitle(var2)
}
new_list = c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster", "Gardnerella_vaginalis", "Lachnospiraceae_BVAB1", "Lactobacillus_gasseri_cluster")
perf <- data.frame()
predFrame <- data.frame()
see <- subsetFull(full_set, names(t_otu))
updated <- t_otu[,(names(t_otu) %in% names(see))]
#comb <- data.frame()
for (taxa in names(updated)){
mixed_full <- covs.taxa(see, taxa, "mixed", vars)
if (taxa %in% new_list){
orig <- paste(taxa, "Orig", sep = "_")
predicted <- paste(taxa, "Pred", sep = "_")
pre_dat <- getData(mixed_full)
pred <- predict(mixed_full)
comb[1:length(pred),c(orig, predicted)] <- cbind(pre_dat[,c("tax_prop", "SUBJECT_ID")], pred)
# comb <- comb %>%
# rename(!!orig := tax_prop, !!predicted := pred)
}
var = paste(taxa, "Full", sep = "_")
predFrame[1:length(predict(mixed_full)),taxa] <- predict(mixed_full)
perf[taxa,names(model_performance(mixed_full, metrics = "all", verbose = TRUE))] <- model_performance(mixed_full, metrics = "all", verbose = TRUE)
}
predFrame[predFrame < 0] <- 0
predFrame[predFrame > 1] <- 1
comb[comb < 0] <- 0
comb[comb > 1] <- 1
comb <- comb[ , c(2, 1, 3:ncol(comb))]
no_na <- na.omit(full_set[, vars])
pred_Full <- cbind(predFrame, no_na)
#Get the actual from the initial model
#Add in subject_id
#Truncate, DONE
perf$rankRMSE <- rank(perf$RMSE)
perf$rankR2Cond <- rank(-perf$R2_conditional)
perf$Name <- rownames(perf)
kable(perf, table.attr = "style = \"color: black;\"") %>%
kable_styling(bootstrap_options = c("striped", "hover"))
| |
AIC |
BIC |
R2_conditional |
R2_marginal |
ICC |
RMSE |
rankRMSE |
rankR2Cond |
Name |
| Acidovorax_delafieldii_temperans |
-17387.410 |
-16959.236 |
0.9271641 |
0.2295530 |
0.9054628 |
0.0007071 |
8 |
14 |
Acidovorax_delafieldii_temperans |
| Actinobaculum_massiliense |
-15720.057 |
-15291.883 |
0.1877250 |
0.1087328 |
0.0886291 |
0.0020474 |
43 |
112 |
Actinobaculum_massiliense |
| Actinomyces_meyeri_odontolyticus |
-16192.741 |
-15764.567 |
0.2221724 |
0.2200070 |
0.0027761 |
0.0019171 |
40 |
107 |
Actinomyces_meyeri_odontolyticus |
| Actinomyces_oris |
-17914.653 |
-17486.479 |
NA |
0.2045212 |
NA |
0.0011628 |
17 |
134 |
Actinomyces_oris |
| Actinomyces_turicensis |
-17145.296 |
-16717.122 |
0.7263134 |
0.7259751 |
0.0012345 |
0.0014539 |
26 |
32 |
Actinomyces_turicensis |
| Actinomyces_viscosus |
-23121.373 |
-22693.199 |
NA |
0.9991278 |
NA |
0.0002544 |
3 |
135 |
Actinomyces_viscosus |
| Aerococcus_christensenii |
-11549.295 |
-11121.121 |
0.3151074 |
0.1628666 |
0.1818596 |
0.0064874 |
108 |
96 |
Aerococcus_christensenii |
| Alistipes_putredinis |
-17319.223 |
-16891.049 |
0.2937339 |
0.0347617 |
0.2682988 |
0.0011426 |
16 |
101 |
Alistipes_putredinis |
| Alloprevotella_tannerae |
-17248.765 |
-16820.591 |
0.6059447 |
0.6032216 |
0.0068629 |
0.0014031 |
24 |
55 |
Alloprevotella_tannerae |
| Alloscardovia_omnicolens |
-19051.850 |
-18623.676 |
0.9764949 |
0.9704455 |
0.2046855 |
0.0007158 |
9 |
9 |
Alloscardovia_omnicolens |
| Anaerococcus_hydrogenalis |
-14922.757 |
-14494.582 |
0.3844899 |
0.3349658 |
0.0744685 |
0.0026119 |
55 |
83 |
Anaerococcus_hydrogenalis |
| Anaerococcus_lactolyticus_cluster |
-12227.660 |
-11799.486 |
0.2947407 |
0.1989324 |
0.1196008 |
0.0055480 |
98 |
100 |
Anaerococcus_lactolyticus_cluster |
| Anaerococcus_OTU147 |
-13373.748 |
-12945.574 |
0.6903183 |
0.6016931 |
0.2225047 |
0.0037140 |
77 |
41 |
Anaerococcus_OTU147 |
| Anaerococcus_OTU153 |
-13596.832 |
-13168.658 |
0.0251648 |
0.0222535 |
0.0029775 |
0.0040891 |
85 |
132 |
Anaerococcus_OTU153 |
| Anaerococcus_OTU5 |
-14646.079 |
-14217.905 |
0.4881823 |
0.4521661 |
0.0657429 |
0.0028510 |
57 |
73 |
Anaerococcus_OTU5 |
| Anaerococcus_tetradius |
-14025.857 |
-13597.683 |
0.2405319 |
0.1324379 |
0.1245951 |
0.0032709 |
68 |
106 |
Anaerococcus_tetradius |
| Anaerococcus_vaginalis |
-14494.086 |
-14065.912 |
0.2064371 |
0.2048119 |
0.0020438 |
0.0031497 |
66 |
109 |
Anaerococcus_vaginalis |
| Anaeroglobus_geminatus |
-21037.536 |
-20609.362 |
0.9351840 |
0.9272559 |
0.1089851 |
0.0004272 |
6 |
13 |
Anaeroglobus_geminatus |
| Arthrobacter_albus_cumminsii |
-14539.968 |
-14111.794 |
0.2168729 |
0.1840776 |
0.0401941 |
0.0030028 |
61 |
108 |
Arthrobacter_albus_cumminsii |
| Atopobium_rimae |
-15363.122 |
-14934.948 |
NA |
0.0219892 |
NA |
0.0024489 |
53 |
136 |
Atopobium_rimae |
| Atopobium_vaginae |
-5190.115 |
-4761.940 |
0.7177193 |
0.6678172 |
0.1502248 |
0.0423779 |
143 |
38 |
Atopobium_vaginae |
| Bacteroides_cellulosilyticus_intestinalis_oleiciplenus |
-19367.897 |
-18939.723 |
0.9865959 |
0.9864790 |
0.0086461 |
0.0007546 |
10 |
7 |
Bacteroides_cellulosilyticus_intestinalis_oleiciplenus |
| Bacteroides_coagulans |
-13653.838 |
-13225.664 |
0.4043407 |
0.3414861 |
0.0954491 |
0.0037232 |
78 |
79 |
Bacteroides_coagulans |
| Bacteroides_fragilis |
-17094.283 |
-16666.109 |
0.9927665 |
0.9448892 |
0.8687463 |
0.0008135 |
11 |
4 |
Bacteroides_fragilis |
| Bacteroides_massiliensis |
-15873.587 |
-15445.413 |
0.3121851 |
0.3077049 |
0.0064716 |
0.0020969 |
46 |
97 |
Bacteroides_massiliensis |
| Bacteroides_stercoris |
-14477.036 |
-14048.861 |
0.0297209 |
0.0267779 |
0.0030240 |
0.0031625 |
67 |
130 |
Bacteroides_stercoris |
| Bacteroides_uniformis |
-16756.746 |
-16328.572 |
0.0933740 |
0.0891582 |
0.0046285 |
0.0016232 |
32 |
122 |
Bacteroides_uniformis |
| Bacteroides_vulgatus_dorei |
-12015.922 |
-11587.748 |
0.7287686 |
0.7255546 |
0.0117110 |
0.0064336 |
107 |
30 |
Bacteroides_vulgatus_dorei |
| Bergeyella_sp_oral_taxon_931 |
-12982.393 |
-12554.219 |
0.3028418 |
0.0359770 |
0.2768241 |
0.0040319 |
84 |
99 |
Bergeyella_sp_oral_taxon_931 |
| Bifidobacterium_bifidum |
-13471.207 |
-13043.033 |
0.1020659 |
0.0750877 |
0.0291684 |
0.0041413 |
86 |
121 |
Bifidobacterium_bifidum |
| Bifidobacterium_breve_cluster |
-11078.986 |
-10650.811 |
0.7760194 |
0.7646163 |
0.0484448 |
0.0081894 |
116 |
27 |
Bifidobacterium_breve_cluster |
| Bifidobacterium_dentium |
-14408.210 |
-13980.035 |
0.3088783 |
0.0270391 |
0.2896716 |
0.0026397 |
56 |
98 |
Bifidobacterium_dentium |
| Bifidobacterium_longum_infantis_suis |
-11635.179 |
-11207.005 |
0.9627866 |
0.9591566 |
0.0888780 |
0.0067442 |
112 |
10 |
Bifidobacterium_longum_infantis_suis |
| Bosea_cluster53 |
-15841.045 |
-15412.871 |
0.0337541 |
0.0094344 |
0.0245513 |
0.0020821 |
44 |
129 |
Bosea_cluster53 |
| Bradyrhizobiaceae_cluster49 |
-15389.722 |
-14961.548 |
0.9070597 |
0.9044323 |
0.0274927 |
0.0023691 |
51 |
15 |
Bradyrhizobiaceae_cluster49 |
| Brevibacterium_ravenspurgense |
-16031.641 |
-15603.467 |
0.2047684 |
0.0859569 |
0.1299846 |
0.0018146 |
38 |
110 |
Brevibacterium_ravenspurgense |
| Campylobacter_hominis |
-15670.135 |
-15241.961 |
0.7217163 |
0.7027838 |
0.0636992 |
0.0021178 |
47 |
36 |
Campylobacter_hominis |
| Campylobacter_ureolyticus |
-10874.731 |
-10446.557 |
0.3750741 |
0.3215450 |
0.0788985 |
0.0084842 |
117 |
84 |
Campylobacter_ureolyticus |
| Caulobacteraceae_cluster34 |
-18483.008 |
-18054.834 |
0.2406715 |
0.2391469 |
0.0020038 |
0.0009832 |
13 |
105 |
Caulobacteraceae_cluster34 |
| Clostridiaceae_1_OTU17 |
-14981.602 |
-14553.428 |
0.4267996 |
0.0927837 |
0.3681767 |
0.0021368 |
48 |
77 |
Clostridiaceae_1_OTU17 |
| Clostridiales_BVAB2 |
-10144.740 |
-9716.565 |
0.3568899 |
0.1961364 |
0.1999760 |
0.0096642 |
119 |
87 |
Clostridiales_BVAB2 |
| Clostridiales_BVAB3 |
-15805.018 |
-15376.844 |
0.5740039 |
0.5219274 |
0.1089300 |
0.0019677 |
42 |
62 |
Clostridiales_BVAB3 |
| Clostridiales_OTU22 |
-11204.319 |
-10776.145 |
0.4332410 |
0.3862199 |
0.0766091 |
0.0077196 |
115 |
76 |
Clostridiales_OTU22 |
| Clostridium_colicanis |
-16601.161 |
-16172.987 |
0.5398366 |
0.1221161 |
0.4758266 |
0.0012553 |
20 |
63 |
Clostridium_colicanis |
| Coriobacteriaceae_OTU27 |
-11870.674 |
-11442.500 |
0.3226097 |
0.2985736 |
0.0342674 |
0.0065781 |
110 |
92 |
Coriobacteriaceae_OTU27 |
| Corynebacterium_aurimucosum_nigricans |
-12280.496 |
-11852.322 |
0.6031171 |
0.5820755 |
0.0503479 |
0.0057579 |
101 |
56 |
Corynebacterium_aurimucosum_nigricans |
| Corynebacterium_cluster45 |
-9984.212 |
-9556.037 |
0.6365505 |
0.6072596 |
0.0745807 |
0.0110394 |
123 |
49 |
Corynebacterium_cluster45 |
| Corynebacterium_cluster58 |
-10123.409 |
-9695.234 |
0.6697698 |
0.6390697 |
0.0850582 |
0.0105152 |
122 |
44 |
Corynebacterium_cluster58 |
| Corynebacterium_coyleae |
-13420.777 |
-12992.603 |
0.2844032 |
0.1643998 |
0.1436135 |
0.0038522 |
81 |
102 |
Corynebacterium_coyleae |
| Corynebacterium_imitans_lipophiloflavum |
-12886.473 |
-12458.299 |
0.1650645 |
0.1366723 |
0.0328869 |
0.0048961 |
92 |
116 |
Corynebacterium_imitans_lipophiloflavum |
| Corynebacterium_pyruviciproducens_cluster |
-13655.134 |
-13226.960 |
0.5125039 |
0.4412694 |
0.1274934 |
0.0036374 |
76 |
68 |
Corynebacterium_pyruviciproducens_cluster |
| Corynebacterium_simulans_striatum |
-16261.320 |
-15833.146 |
0.1337907 |
0.0947030 |
0.0431767 |
0.0018123 |
37 |
119 |
Corynebacterium_simulans_striatum |
| Corynebacterium_thomssenii_sundsvallense |
-11479.381 |
-11051.207 |
0.5206713 |
0.3074384 |
0.3078901 |
0.0061423 |
105 |
66 |
Corynebacterium_thomssenii_sundsvallense |
| Corynebacterium_urealyticum |
-17645.668 |
-17217.494 |
0.0854713 |
0.0692736 |
0.0174033 |
0.0012375 |
19 |
124 |
Corynebacterium_urealyticum |
| Delftia_acidovorans_lacustris_tsuruhatensis |
-39739.438 |
-39311.264 |
1.0000000 |
1.0000000 |
0.6269149 |
0.0000013 |
1 |
1 |
Delftia_acidovorans_lacustris_tsuruhatensis |
| Dialister_cluster51 |
-10347.769 |
-9919.595 |
0.5386496 |
0.4972450 |
0.0823553 |
0.0098688 |
121 |
64 |
Dialister_cluster51 |
| Dialister_invisus |
-18254.521 |
-17826.347 |
0.8110256 |
0.7648935 |
0.1962178 |
0.0009081 |
12 |
22 |
Dialister_invisus |
| Dialister_micraerophilus |
-11942.287 |
-11514.113 |
0.3590883 |
0.2684235 |
0.1239308 |
0.0060117 |
104 |
85 |
Dialister_micraerophilus |
| Dialister_propionicifaciens |
-12741.237 |
-12313.063 |
0.4948088 |
0.3573199 |
0.2139305 |
0.0044904 |
91 |
71 |
Dialister_propionicifaciens |
| Dorea_longicatena |
-14479.415 |
-14051.241 |
0.1611618 |
0.0676141 |
0.1003315 |
0.0029154 |
59 |
117 |
Dorea_longicatena |
| Enterobacteriaceae_cluster31 |
-8430.659 |
-8002.485 |
0.6906619 |
0.6890979 |
0.0050307 |
0.0184357 |
135 |
40 |
Enterobacteriaceae_cluster31 |
| Enterococcus_faecalis |
-15801.895 |
-15373.721 |
0.9478028 |
0.9461683 |
0.0303633 |
0.0020952 |
45 |
11 |
Enterococcus_faecalis |
| Ezakiella_peruensis |
-13682.122 |
-13253.948 |
0.6743047 |
0.4525639 |
0.4050533 |
0.0030598 |
62 |
42 |
Ezakiella_peruensis |
| Faecalibacterium_prausnitzii |
-19839.623 |
-19411.449 |
0.9836472 |
0.9834699 |
0.0107273 |
0.0006563 |
7 |
8 |
Faecalibacterium_prausnitzii |
| Fenollaria_timonensis |
-15767.716 |
-15339.542 |
0.6089678 |
0.4477681 |
0.2919058 |
0.0017728 |
34 |
54 |
Fenollaria_timonensis |
| Finegoldia_magna |
-9461.447 |
-9033.272 |
0.4884036 |
0.4479662 |
0.0732517 |
0.0128724 |
130 |
72 |
Finegoldia_magna |
| Fusobacterium_cluster48 |
-11778.202 |
-11350.028 |
0.5181079 |
0.4763256 |
0.0797868 |
0.0065131 |
109 |
67 |
Fusobacterium_cluster48 |
| Fusobacterium_gonidiaformans_equinum |
-13511.169 |
-13082.995 |
0.6135764 |
0.6083021 |
0.0134652 |
0.0041515 |
87 |
52 |
Fusobacterium_gonidiaformans_equinum |
| Gardnerella_vaginalis |
-3526.012 |
-3097.837 |
0.7247044 |
0.5923336 |
0.3247037 |
0.0620045 |
145 |
33 |
Gardnerella_vaginalis |
| Gemella_morbillorum_sanguinis_haemolysans |
-11713.301 |
-11285.127 |
0.3567435 |
0.3519466 |
0.0074020 |
0.0070561 |
113 |
88 |
Gemella_morbillorum_sanguinis_haemolysans |
| Gemella_OTU86 |
-12681.749 |
-12253.575 |
NA |
0.6045299 |
NA |
0.0053563 |
97 |
137 |
Gemella_OTU86 |
| Haemophilus_cluster60 |
-12617.651 |
-12189.477 |
0.1875526 |
0.1609860 |
0.0316640 |
0.0053014 |
95 |
113 |
Haemophilus_cluster60 |
| Helcococcus_seattlensis |
-17137.023 |
-16708.848 |
NA |
0.6058945 |
NA |
0.0014591 |
27 |
138 |
Helcococcus_seattlensis |
| Lachnospiraceae_BVAB1 |
-3634.694 |
-3206.520 |
0.8921398 |
0.8758694 |
0.1310750 |
0.0676006 |
146 |
18 |
Lachnospiraceae_BVAB1 |
| Lachnospiraceae_OTU33 |
-16458.715 |
-16030.541 |
0.3214496 |
0.1890243 |
0.1632913 |
0.0015665 |
28 |
93 |
Lachnospiraceae_OTU33 |
| Lactobacillus_crispatus_cluster |
-3036.883 |
-2608.709 |
0.9435135 |
0.9278223 |
0.2173966 |
0.0761257 |
147 |
12 |
Lactobacillus_crispatus_cluster |
| Lactobacillus_delbrueckii |
-12241.633 |
-11813.459 |
0.9905929 |
0.9662504 |
0.7212673 |
0.0038535 |
82 |
5 |
Lactobacillus_delbrueckii |
| Lactobacillus_gasseri_cluster |
-4971.098 |
-4542.924 |
0.8658581 |
0.7717616 |
0.4122726 |
0.0387450 |
142 |
20 |
Lactobacillus_gasseri_cluster |
| Lactobacillus_iners |
-1813.793 |
-1385.619 |
0.8925608 |
0.8564098 |
0.2517650 |
0.1065701 |
148 |
17 |
Lactobacillus_iners |
| Lactobacillus_jensenii |
-4259.587 |
-3831.413 |
0.7961473 |
0.6668447 |
0.3881152 |
0.0483247 |
144 |
24 |
Lactobacillus_jensenii |
| Megasphaera_OTU70_type1 |
-7467.563 |
-7039.389 |
0.3477399 |
0.2288477 |
0.1541748 |
0.0217420 |
137 |
89 |
Megasphaera_OTU70_type1 |
| Megasphaera_OTU71_type2 |
-11234.891 |
-10806.717 |
0.5791815 |
0.5164580 |
0.1297169 |
0.0073608 |
114 |
60 |
Megasphaera_OTU71_type2 |
| Metaprevotella_massiliensis |
-13811.086 |
-13382.912 |
0.3464414 |
0.3280576 |
0.0273591 |
0.0037562 |
79 |
90 |
Metaprevotella_massiliensis |
| Moraxella_osloensis |
-13329.425 |
-12901.251 |
0.0088088 |
0.0032703 |
0.0055567 |
0.0044102 |
90 |
133 |
Moraxella_osloensis |
| Morganella_morganii |
-18406.099 |
-17977.925 |
0.9930131 |
0.9930050 |
0.0011503 |
0.0010063 |
14 |
3 |
Morganella_morganii |
| Moryella_indoligenes |
-13804.123 |
-13375.949 |
0.3904462 |
0.3883232 |
0.0034708 |
0.0038472 |
80 |
82 |
Moryella_indoligenes |
| Mycoplasma_girerdii |
-8433.902 |
-8005.727 |
NA |
0.8597742 |
NA |
0.0185073 |
136 |
139 |
Mycoplasma_girerdii |
| Mycoplasma_hominis |
-8821.780 |
-8393.606 |
0.7451051 |
0.7057186 |
0.1338397 |
0.0148453 |
131 |
29 |
Mycoplasma_hominis |
| Negativicoccus_succinicivorans |
-14828.361 |
-14400.187 |
0.1354207 |
0.1343746 |
0.0012086 |
0.0028592 |
58 |
118 |
Negativicoccus_succinicivorans |
| Neisseria_flava/mucosa/pharyngis/sicca |
-16846.954 |
-16418.780 |
0.7819320 |
0.7803593 |
0.0071605 |
0.0015772 |
29 |
26 |
Neisseria_flava/mucosa/pharyngis/sicca |
| Neisseria_flavescens |
-15206.405 |
-14778.231 |
0.0809069 |
0.0774925 |
0.0037012 |
0.0025544 |
54 |
126 |
Neisseria_flavescens |
| Oligella_urethralis |
-13361.349 |
-12933.175 |
0.7244324 |
0.7211371 |
0.0118168 |
0.0043437 |
89 |
34 |
Oligella_urethralis |
| Parasutterella_excrementihominis |
-21400.997 |
-20972.823 |
0.9883619 |
0.9880825 |
0.0234445 |
0.0004113 |
5 |
6 |
Parasutterella_excrementihominis |
| Parvimonas_OTU142 |
-14041.327 |
-13613.153 |
0.3307974 |
0.2446606 |
0.1140372 |
0.0032805 |
69 |
91 |
Parvimonas_OTU142 |
| Peptoniphilus_coxii |
-17929.505 |
-17501.330 |
0.3169154 |
0.2017410 |
0.1442820 |
0.0010327 |
15 |
94 |
Peptoniphilus_coxii |
| Peptoniphilus_duerdenii |
-14219.740 |
-13791.566 |
0.6622962 |
0.5918521 |
0.1725946 |
0.0029932 |
60 |
45 |
Peptoniphilus_duerdenii |
| Peptoniphilus_harei |
-14431.342 |
-14003.168 |
0.4231846 |
0.3902277 |
0.0540479 |
0.0030640 |
63 |
78 |
Peptoniphilus_harei |
| Peptoniphilus_indolicus |
-13371.390 |
-12943.216 |
0.5904156 |
0.5097682 |
0.1645088 |
0.0038544 |
83 |
57 |
Peptoniphilus_indolicus |
| Peptoniphilus_urinimassiliensis |
-16674.119 |
-16245.944 |
NA |
0.6422790 |
NA |
0.0016702 |
33 |
140 |
Peptoniphilus_urinimassiliensis |
| Peptostreptococcus_anaerobius |
-15527.769 |
-15099.594 |
0.7970001 |
0.7961226 |
0.0043040 |
0.0023243 |
50 |
23 |
Peptostreptococcus_anaerobius |
| Porphyromonadaceae_OTU134 |
-13993.871 |
-13565.697 |
0.6371127 |
0.6003435 |
0.0920020 |
0.0033800 |
70 |
48 |
Porphyromonadaceae_OTU134 |
| Porphyromonas_bennonis |
-12350.176 |
-11922.002 |
0.4955297 |
0.4817128 |
0.0266589 |
0.0057571 |
100 |
70 |
Porphyromonas_bennonis |
| Porphyromonas_endodontalis |
-17026.343 |
-16598.169 |
0.7710487 |
0.7568240 |
0.0584954 |
0.0014314 |
25 |
28 |
Porphyromonas_endodontalis |
| Porphyromonas_somerae |
-12378.075 |
-11949.901 |
0.6273122 |
0.6170711 |
0.0267441 |
0.0057100 |
99 |
51 |
Porphyromonas_somerae |
| Porphyromonas_uenonis_asaccharolytica |
-16090.012 |
-15661.838 |
0.8251491 |
0.1553773 |
0.7929834 |
0.0011826 |
18 |
21 |
Porphyromonas_uenonis_asaccharolytica |
| Prevotella_amnii |
-8677.040 |
-8248.866 |
0.4499903 |
0.4229423 |
0.0468722 |
0.0165311 |
132 |
74 |
Prevotella_amnii |
| Prevotella_bergensis |
-16186.624 |
-15758.450 |
NA |
0.1950469 |
NA |
0.0019256 |
41 |
141 |
Prevotella_bergensis |
| Prevotella_bivia |
-6950.805 |
-6522.630 |
0.6510750 |
0.6285707 |
0.0605885 |
0.0270558 |
139 |
47 |
Prevotella_bivia |
| Prevotella_cluster2 |
-5822.643 |
-5394.469 |
0.7178322 |
0.6851120 |
0.1039107 |
0.0363862 |
141 |
37 |
Prevotella_cluster2 |
| Prevotella_cluster50 |
-10235.123 |
-9806.949 |
0.4344745 |
0.2877211 |
0.2060336 |
0.0093773 |
118 |
75 |
Prevotella_cluster50 |
| Prevotella_copri |
-17403.038 |
-16974.864 |
NA |
0.8906897 |
NA |
0.0013501 |
23 |
142 |
Prevotella_copri |
| Prevotella_corporis |
-11738.854 |
-11310.680 |
0.6731350 |
0.6273574 |
0.1228460 |
0.0063843 |
106 |
43 |
Prevotella_corporis |
| Prevotella_denticola |
-12693.957 |
-12265.782 |
0.1223159 |
0.0517441 |
0.0744227 |
0.0050061 |
93 |
120 |
Prevotella_denticola |
| Prevotella_disiens |
-9677.822 |
-9249.648 |
0.5740524 |
0.5239280 |
0.1052875 |
0.0117979 |
129 |
61 |
Prevotella_disiens |
| Prevotella_melaninogenica_cluster |
-10027.073 |
-9598.899 |
0.5069715 |
0.4842113 |
0.0441269 |
0.0111728 |
126 |
69 |
Prevotella_melaninogenica_cluster |
| Prevotella_multiformis |
-16367.291 |
-15939.117 |
0.0258672 |
0.0167734 |
0.0092490 |
0.0018107 |
36 |
131 |
Prevotella_multiformis |
| Prevotella_oris |
-13855.232 |
-13427.058 |
0.5802765 |
0.5437766 |
0.0800044 |
0.0035516 |
72 |
59 |
Prevotella_oris |
| Prevotella_OTU-T3 |
-12322.620 |
-11894.446 |
0.1655112 |
0.1411768 |
0.0283346 |
0.0057950 |
103 |
115 |
Prevotella_OTU-T3 |
| Prevotella_OTU44 |
-13504.712 |
-13076.538 |
0.3165165 |
0.1354243 |
0.2094579 |
0.0036033 |
75 |
95 |
Prevotella_OTU44 |
| Prevotella_OTU49 |
-15416.529 |
-14988.355 |
0.0825019 |
0.0675626 |
0.0160218 |
0.0023749 |
52 |
125 |
Prevotella_OTU49 |
| Prevotella_OTU54 |
-17478.489 |
-17050.315 |
0.0921929 |
0.0830236 |
0.0099996 |
0.0013082 |
21 |
123 |
Prevotella_OTU54 |
| Prevotella_pleuritidis |
-16390.591 |
-15962.417 |
0.1967202 |
0.1876299 |
0.0111898 |
0.0017952 |
35 |
111 |
Prevotella_pleuritidis |
| Prevotellaceae_OTU61 |
-13630.966 |
-13202.792 |
0.2570843 |
0.1103191 |
0.1649638 |
0.0035721 |
74 |
104 |
Prevotellaceae_OTU61 |
| Propionibacterium_acnes |
-12343.547 |
-11915.373 |
0.7890623 |
0.7835592 |
0.0254255 |
0.0057745 |
102 |
25 |
Propionibacterium_acnes |
| Propionibacterium_avidum_propionicum |
-22634.687 |
-22206.513 |
0.5900243 |
0.5700952 |
0.0463571 |
0.0002813 |
4 |
58 |
Propionibacterium_avidum_propionicum |
| Propionimicrobium_lymphophilum |
-15415.931 |
-14987.757 |
0.3580599 |
0.2975746 |
0.0861092 |
0.0022417 |
49 |
86 |
Propionimicrobium_lymphophilum |
| Proteobacteria_OTU-T1 |
-11803.483 |
-11375.309 |
0.3916603 |
0.0059571 |
0.3880146 |
0.0053444 |
96 |
81 |
Proteobacteria_OTU-T1 |
| Pseudomonas_aeruginosa |
-28946.442 |
-28518.268 |
0.9994494 |
0.9994403 |
0.0161400 |
0.0000458 |
2 |
2 |
Pseudomonas_aeruginosa |
| Pseudomonas_gessardii_cluster |
-10051.331 |
-9623.157 |
NA |
0.7282996 |
NA |
0.0115428 |
128 |
143 |
Pseudomonas_gessardii_cluster |
| Pyramidobacter_sp |
-17463.554 |
-17035.380 |
NA |
0.1746499 |
NA |
0.0013265 |
22 |
144 |
Pyramidobacter_sp |
| Rothia_mucilaginosa |
-14543.629 |
-14115.455 |
0.3920836 |
0.3891579 |
0.0047896 |
0.0030964 |
64 |
80 |
Rothia_mucilaginosa |
| Shuttleworthia_satelles |
-16847.007 |
-16418.833 |
NA |
0.0235293 |
NA |
0.0015880 |
30 |
145 |
Shuttleworthia_satelles |
| Sneathia_amnii |
-6506.739 |
-6078.565 |
0.6302520 |
0.5494910 |
0.1792661 |
0.0283143 |
140 |
50 |
Sneathia_amnii |
| Sneathia_OTU65 |
-15287.130 |
-14858.956 |
0.7278586 |
0.0364789 |
0.7175554 |
0.0015885 |
31 |
31 |
Sneathia_OTU65 |
| Sneathia_sanguinegens |
-9242.268 |
-8814.094 |
0.6103573 |
0.3267148 |
0.4212813 |
0.0110825 |
124 |
53 |
Sneathia_sanguinegens |
| Staphylococcus_cluster47 |
-9972.464 |
-9544.290 |
0.9052482 |
0.9025158 |
0.0280285 |
0.0115103 |
127 |
16 |
Staphylococcus_cluster47 |
| Stenotrophomonas_rhizophila |
-14132.455 |
-13704.281 |
0.2580453 |
0.2554337 |
0.0035075 |
0.0034955 |
71 |
103 |
Stenotrophomonas_rhizophila |
| Streptococcus_agalactiae |
-9996.456 |
-9568.282 |
0.8911707 |
0.8841266 |
0.0607908 |
0.0111201 |
125 |
19 |
Streptococcus_agalactiae |
| Streptococcus_anginosus |
-10201.064 |
-9772.889 |
0.7218227 |
0.6671497 |
0.1642571 |
0.0097254 |
120 |
35 |
Streptococcus_anginosus |
| Streptococcus_cluster29 |
-7157.037 |
-6728.863 |
NA |
0.7616446 |
NA |
0.0268661 |
138 |
146 |
Streptococcus_cluster29 |
| Streptococcus_parasanguinis |
-14245.346 |
-13817.172 |
0.1813025 |
0.0935078 |
0.0968511 |
0.0031295 |
65 |
114 |
Streptococcus_parasanguinis |
| Streptococcus_salivarius_thermophilus_vestibularis |
-12741.409 |
-12313.235 |
NA |
0.2848975 |
NA |
0.0052639 |
94 |
147 |
Streptococcus_salivarius_thermophilus_vestibularis |
| Sutterella_sanguinis |
-16261.200 |
-15833.026 |
0.0779703 |
0.0603826 |
0.0187179 |
0.0018514 |
39 |
127 |
Sutterella_sanguinis |
| TM7_OTU-H1 |
-8029.654 |
-7601.480 |
0.5272706 |
0.4295954 |
0.1712384 |
0.0182472 |
134 |
65 |
TM7_OTU-H1 |
| Ureaplasma_cluster23 |
-8382.795 |
-7954.621 |
0.7026208 |
0.6466340 |
0.1584385 |
0.0165980 |
133 |
39 |
Ureaplasma_cluster23 |
| Veillonella_cluster61 |
-11907.828 |
-11479.654 |
NA |
0.2620593 |
NA |
0.0067139 |
111 |
148 |
Veillonella_cluster61 |
| Veillonella_montpellierensis |
-14048.875 |
-13620.701 |
0.0577799 |
0.0511873 |
0.0069483 |
0.0035701 |
73 |
128 |
Veillonella_montpellierensis |
| Weeksella_virosa |
-13505.758 |
-13077.583 |
0.6554187 |
0.6517386 |
0.0105671 |
0.0041692 |
88 |
46 |
Weeksella_virosa |
#make boxplot for the metrics and look at outliers, use rmse
#see rank of metrics for specific taxa of interest
#add column for quantiles for the specific taxa of interest
rmse <- ggplot(perf, aes(x="RMSE", y=RMSE)) +
geom_boxplot() +
geom_point(data=subset(perf, rownames(perf) %in% new_list),
aes(x="RMSE", y=RMSE),
color="red", size=5) +
geom_text_repel(data=subset(perf, rownames(perf) %in% new_list), aes(label=Name))
rmse

r2_cond <- ggplot(perf, aes(x="R2_Cond", y=R2_conditional)) +
geom_boxplot() +
geom_point(data=subset(perf, rownames(perf) %in% new_list),
aes(x="R2_Cond", y=R2_conditional),
color="red", size=5) +
geom_text_repel(data=subset(perf, rownames(perf) %in% new_list), aes(label=Name))
r2_cond

marrangeGrob(tax_plots, nrow = 2, ncol = 2)





marrangeGrob(tax_hists, nrow = 2, ncol = 2)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.







iners_f_hist <- make_hists(iners_fixed, "Lactobacillus_iners", full_set, vars) + ggtitle("Iners Fixed Plot")
iners_m_hist <- make_hists(iners_mixed, "Lactobacillus_iners", full_set, mx_vars) + ggtitle("Iners Mixed Plot")
ggarrange(iners_f_hist, iners_m_hist)
crisp_f_hist <- make_hists(crisp_fixed, "Lactobacillus_crispatus_cluster", full_set, vars) + ggtitle("Crisp Fixed Plot")
crisp_m_hist <- make_hists(crisp_mixed, "Lactobacillus_crispatus_cluster", full_set, mx_vars) + ggtitle("Crisp Mixed Plot")
ggarrange(crisp_f_hist, crisp_m_hist)
#clean age, set blanks to NA, clean values, make 18-28 reference group
# do individual plots
# quantify prediction success
# get model summaries
# perform on more taxa
#education, high_school_ged is baseline, put in order of ascending degree
#predictions <- pred_plot(full_set, mod_Iners)
#lapply(models, model_plot)
mod_Crisp <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "class", "fixed")
mod_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "class", "fixed")
mod_BVAB <- taxa.glm(full_set, "Lachnospiraceae_BVAB1", "class", "fixed")
mod_Gard <- taxa.glm(full_set, "Gardnerella_vaginalis", "class", "fixed")
mod_Gass <- taxa.glm(full_set, "Lactobacillus_gasseri_cluster", "class", "fixed")
prop_Crisp <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "prop", "fixed")
prop_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "prop", "fixed")
prop_BVAB <- taxa.glm(full_set, "Lachnospiraceae_BVAB1", "prop", "fixed")
prop_Gard <- taxa.glm(full_set, "Gardnerella_vaginalis", "prop", "fixed")
prop_Gass <- taxa.glm(full_set, "Lactobacillus_gasseri_cluster", "prop", "fixed")
ggarrange(model_plot(mod_Crisp), model_plot(prop_Crisp),labels = c("Class Crisp", "Prop Crisp"))
ggarrange(model_plot(mod_Iners), model_plot(prop_Iners),labels = c("Class Iners", "Prop Iners"))
ggarrange(model_plot(mod_BVAB), model_plot(prop_BVAB),labels = c("Class BVAB", "Prop BVAB"))
ggarrange(model_plot(mod_Crisp), model_plot(prop_Iners),labels = c("Class Gard", "Prop Gard"))
ggarrange(model_plot(mod_Gass), model_plot(prop_Gass),labels = c("Class Gass", "Prop Gass"))
model_plot(mod_Crisp) + ggtitle("Class Crispatus")
model_plot(mod_Iners) + ggtitle("Class Iners")
model_plot(mod_BVAB) + ggtitle("Class BVAB")
model_plot(mod_Gard) + ggtitle("Class Gardnerella")
model_plot(mod_Gass) + ggtitle("Class Gasseri")
#alpha_div and vagitype show up in almost every model vaginal_pH in most of them
#correlations between data
#longitudinal plots when stratifying by covariates
#count and proportion plots
#Look at model pre and post aic
#ggaly and regular correlation matrix
#Look at distribution of NA's in covariate (pre-filtering) and see if it affects model by removing it
mixed_Iners <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "mean", "mixed")
#Some linear combination of the parameters perfectly separates failure from successes, I assume it's vagitype
#Doesn't run still
na.test <- function (x) {
w <- sapply(x, function(x)all(is.na(x)))
if (any(w)) {
stop(paste("All NA in columns", paste(which(w), collapse=", ")))
}
}
#Recode High to 1 and Low to 0
full_set[["class_Iners"]] <- recode(full_set[["class_Iners"]], "High"= 1, "Low"= 0)
full_set$class_Iners <- as.factor(full_set$class_Iners)
covs <- c("class_Iners", "vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv", "phys", "antibiotics_current","SUBJECT_ID")
model_data <- full_set[,covs]
no.na.data <- na.omit(model_data)
reduced_mod <- glmer(class_Iners ~ alpha_div + vagitype + (1|SUBJECT_ID), data = no.na.data, family = binomial, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5))) #Unable to evaluate scaled gradient, model fails to converge
step.model <- stepAIC(log_mod, direction = "both",
trace = FALSE)
#install.packages("ResourceSelection")
library(ResourceSelection)
fixed_mod <- glm(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + phys + bv + antibiotics_current, data = no.na.data, family = binomial)
step.model <- stepAIC(fixed_mod, direction = "both",
trace = FALSE)
step.back <- stepAIC(fixed_mod, direction = "backward")
hoslem.test(no.na.data$class_Iners, fitted(fixed_mod))
hoslem.test(no.na.data$class_Iners, fitted(step.model))
# model becomes class_Iners ~ alpha_div + vagitype
predict(fixed_mod, type = "response")
histogram(predict(fixed_mod, type = "response"))
plot(predict(fixed_mod, type = "response"), full_set$class_Iners)
plotting_dfm <- expand.grid(alpha_div = seq(0, 3.5, 0.01),
vagitype = c("Lactobacillus_crispatus_cluster", "Lactobacillus_iners", "Lachnospiraceae_BVAB1", "Gardnerella_vaginalis", "Lactobacillus_gasseri_cluster"))
plotting_dfm$preds <- predict(step.model, newdata=plotting_dfm, type = "response")
pl <- ggplot(plotting_dfm, aes(x=alpha_div, y =preds, color=as.factor(vagitype)))
pl +
geom_point( ) +
ggtitle("Predicted High/Low by Alpha_Div and Vagitype")
#Look at alpha div and vagitype over trimesters and see how it changes
# plot those who change
#Table of amt that change vs not change
reduced_mod <- glmer(class_Crisp ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
reduced_mod <- glmer(class_Gass ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
reduced_mod <- glmer(class_Gard ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
reduced_mod <- glmer(class_BVAB ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
library(caret)
#Split Data 80:20
training.samps <- full_set$class_Iners %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + SUBJECT_ID, data = full_set)
#summary(mda_model)
mda_model$percent.explained
mda_model$confusion
covs <- covs[which(names(pred) %in% rownames(full_set)),]
#mda_pred <- predict(mda_model,test.data)
# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_Iners[which(names(pred) %in% rownames(full_set))])
#Random Forest
#try randomforest with formula and see if random var works
#install.packages("randomForest")
#library(randomForest)
#full_set$vagitype <- as.character(full_set$vagitype)
#full_set$SUBJECT_ID <- as.character(full_set$SUBJECT_ID)
#forest_model <- randomForest(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, na.action=na.omit, importance = TRUE)
#Split Data 80:20
training.samps <- full_set$class_Snea %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_Snea ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
mda_model$confusion
covs <- covs[which(names(pred) %in% rownames(full_set)),]
#mda_pred <- predict(mda_model,test.data)
# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_Snea ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_Snea[which(names(pred) %in% rownames(full_set))])
#Split Data 80:20
training.samps <- full_set$class_Prev %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_Prev ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
covs <- covs[which(names(pred) %in% rownames(full_set)),]
mda_model$confusion
#mda_pred <- predict(mda_model,test.data)
# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_Prev ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_Prev[which(names(pred) %in% rownames(full_set))])
#Split Data 80:20
training.samps <- full_set$class_TM %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_TM ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
covs <- covs[which(names(pred) %in% rownames(full_set)),]
mda_model$confusion
#mda_pred <- predict(mda_model,test.data)
# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_TM ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_TM[which(names(pred) %in% rownames(full_set))])
#Split Data 80:20
training.samps <- full_set$class_BVAB %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_BVAB ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
covs <- covs[which(names(pred) %in% rownames(full_set)),]
mda_model$confusion
#mda_pred <- predict(mda_model,test.data)
# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_BVAB ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_BVAB[which(names(pred) %in% rownames(full_set))])
---
title: "Modeling"
output: html_notebook
---
```{r}
rm(list=ls())
require(ggplot2)
#install.packages("pscl")
#install.packages(glmmTMB)
#install.packages("DescTools")
require(DescTools)
require(glmmTMB)
require(pscl)
require(boot)
library(data.table)
library(tidyr)
library(dplyr)
library(stringr)
library(here)
library(phyloseq)
library(vegan)
library(refund)
library(reshape2)
library(kableExtra)
library(lme4)
library(MASS)
library(forcats)
library(GGally)
library(lmerTest)
library(gridExtra)
library(ggfortify)
library(performance)
library(plotly)
library(ggrepel)
library(factoextra)
library(lme4)
#remotes::install_github("palday/coefplot2",
#           subdir = "pkg")
library(coefplot2)
library(ggpubr)

#library(HMP2Data) #install the library from github
```


```{r}
#path to protected dbGap data location

local_path <- "~/Documents/MOMS-PI"# change me
#local_path <- "~/MOMS-PI"# change me

path2 <- file.path(local_path,"71694", "PhenoGenotypeFiles", "RootStudyConsentSet_phs001523.MOMS_PI.v1.p1.c1.DS-PREG-COM-IRB-PUB-MDS", "PhenotypeFiles") 

otu <- read.csv(file = file.path(path2, "MergedFiles", "OTU_table.csv"))
rownames(otu) <- otu$X
otu$X <- NULL 
t_otu <- as.data.frame(t(otu))
meta <- read.csv(file = file.path(path2, "MergedFiles", "Meta_data.csv"))
#physical activity variable
phys <- meta[,98:100]
meta$phys <- ifelse(meta$physical_activity_vigorous == "0_times" | is.na(meta$physical_activity_vigorous), "None", "Vigorous")
meta$phys[which(meta$phys == "None" & meta$physical_activity_moderate != "0_times" & !is.na(meta$physical_activity_moderate))] <- "Moderate"
meta$phys[which(meta$phys == "None" & meta$physical_activity_light != "0_times" & !is.na(meta$physical_activity_light))] <- "Light"
meta$phys <- as.factor(meta$phys)

#filter to just two races
meta <- meta %>%
  filter(race == "african_american" | race == "caucasian")
meta$race <- factor(meta$race)
rownames(meta) <- meta$X
meta$X <- NULL
t_otu <- t_otu[rownames(meta),]
```

```{r EditMeta}
meta <- meta %>%
  mutate(antibiotics_current = replace_na(antibiotics_current, "No"))

meta <- meta %>%
  mutate(infertility_treatment = replace_na(infertility_treatment, "No")) %>%
  mutate(infertility_treatment = recode(infertility_treatment, Not_sure = "No", .default = levels(meta$infertility_treatment)))

meta <- meta %>%
  mutate(vdischarge = replace_na(vdischarge, "No")) %>%
  mutate(vdischarge = recode(vdischarge, Not_Sure = "No", .default = levels(meta$vdischarge)))

meta <- meta %>%
  mutate(vodor = replace_na(vodor, "No")) %>%
  mutate(vodor = recode(vodor, Not_Sure = "No", .default = levels(meta$vodor)))

meta <- meta %>%
  mutate(vitching = replace_na(vitching, "No"))

meta <- meta %>%
  mutate(douche = replace_na(douche, "More_than_1_year_ago_or_never"))

meta <- meta %>%
  mutate(bv_history = replace_na(bv_history, "No")) %>%
  mutate(bv_history = recode(bv_history, Not_Sure = "No", .default = levels(meta$bv_history)))

meta <- meta %>%
  mutate(vaginitis_history = replace_na(vaginitis_history, "No"))

meta <- meta %>%
  mutate(current_medications = replace_na(current_medications, "No")) %>%
  mutate(current_medications = recode(current_medications, No_I_am_not_taking_any_new_medications = "No", .default = levels(meta$current_medications)))

meta <- meta %>%
  mutate(smoker_current = replace_na(smoker_current, "No"))

meta <- meta %>%
  mutate(alcohol_frequency_year = replace_na(alcohol_frequency_year, "0_times"))

meta <- meta %>%
  mutate(diabetes = replace_na(diabetes, "No")) %>%
  mutate(diabetes = recode(diabetes, Not_sure = "No", .default = levels(meta$diabetes)))

meta <- meta %>%
  mutate(no_false_labor = replace_na(no_false_labor, "Yes")) %>%
  mutate(no_false_labor = recode(no_false_labor, No_I_have_not_experienced_false_labor = "Yes", .default = levels(meta$no_false_labor)))

meta <- meta %>%
  mutate(no_high_bp = replace_na(no_high_bp, "Yes"))

meta <- meta %>%
  mutate(no_vbleeding = replace_na(no_vbleeding, "Yes"))

meta <- meta %>%
  mutate(progesterone = replace_na(progesterone, "No")) %>%
  mutate(progesterone = recode(progesterone, Not_Sure = "No", .default = levels(meta$progesterone)))

levels(meta$age)[1] <- NA
```


```{r }
#Create combined table
full_set <- cbind(t_otu, meta)
full_set$alpha_div <- diversity(full_set[,1:length(names(t_otu))], index="shannon")
full_set$num_reads <- rowSums(t_otu)

#Add vagitype
mypropdata <- full_set[,colnames(t_otu)]
mytypes <- apply(mypropdata,1,which.max)
maxprop <- as.numeric(mypropdata[matrix(c(1:nrow(mypropdata),mytypes), ncol=2)])
mytypes <- colnames(mypropdata)[mytypes]
mytypes[maxprop < 0.3] <- "No Type"
mytypes <- as.factor(mytypes)
full_set <- full_set %>%
  mutate(vagitype=mytypes)
high_vagitype <- sort(table(full_set$vagitype), decreasing=T)[1:5]
high_vagitype
#Add proportions
full_set$p_Iners <- full_set$Lactobacillus_iners/full_set$num_reads
full_set$p_Crisp <- full_set$Lactobacillus_crispatus_cluster/full_set$num_reads
full_set$p_Gard <- full_set$Gardnerella_vaginalis/full_set$num_reads
full_set$Gass <- full_set$Lactobacillus_gasseri_cluster/full_set$num_reads
full_set$p_BVAB <- full_set$Lachnospiraceae_BVAB1/full_set$num_reads

```





```{r}
get_mode = function(x, multimodal.na="TRUE"){
  modes <- Mode(x)
  if (multimodal.na=="FALSE" | length(modes)==1) {
    return(modes[1]) 
  } else {
    return(modes[length(modes)+1])
  }
}

#582 subjects
#aggregate at patient level
demog <- aggregate(visit_number ~ SUBJECT_ID, data = meta%>%data.frame, FUN = min)
demog <- merge(demog,meta%>%data.frame, by = c("SUBJECT_ID", "visit_number") )
  
#number visits per subject 
df <- aggregate(visit_number ~ SUBJECT_ID, data = meta%>%data.frame, FUN = length)
colnames(df)[which(colnames(df) == "visit_number")] <- "number of visits"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average income per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(income = get_mode(income, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "income")] <- "average income"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average education per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(education = get_mode(education, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "education")] <- "average education"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average age per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(age = get_mode(age, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "age")] <- "average age"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average prenatal care start per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(prenatal_care_start = get_mode(prenatal_care_start, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "prenatal_care_start")] <- "average p_care_start"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average infertility treatment per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(infertility_treatment = get_mode(infertility_treatment, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "infertility_treatment")] <- "average inf_trt"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average v_discharge per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vdischarge = get_mode(vdischarge, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vdischarge")] <- "average v_discharge"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average v_odor per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vodor = get_mode(vodor, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vodor")] <- "average v_odor"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average v_itching per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vitching = get_mode(vitching, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vitching")] <- "average v_itching"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average douche per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(douche = get_mode(douche, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "douche")] <- "average douche"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average bv_history per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(bv_history = get_mode(bv_history, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "bv_history")] <- "average bv_history"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average vaginitis_history per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(vaginitis_history = get_mode(vaginitis_history, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "vaginitis_history")] <- "average vaginitis_history"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average current_medications per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(current_medications = get_mode(current_medications, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "current_medications")] <- "average current_medications"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average smoker_current per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(smoker_current = get_mode(smoker_current, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "smoker_current")] <- "average smoker_current"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average alcohol_frequency per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(alcohol_frequency_year = get_mode(alcohol_frequency_year, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "alcohol_frequency_year")] <- "average alcohol_frequency"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average diabetes per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(diabetes = get_mode(diabetes, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "diabetes")] <- "average diabetes"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average false_labor per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(no_false_labor = get_mode(no_false_labor, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "no_false_labor")] <- "average no_false_labor"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average high_bp per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(no_high_bp = get_mode(no_high_bp, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "no_high_bp")] <- "average no_high_bp"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average v_bleeding per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(no_vbleeding = get_mode(no_vbleeding, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "no_vbleeding")] <- "average no_vbleeding"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average progesterone per subject 
df <- meta %>% group_by(SUBJECT_ID) %>% summarise(progesterone = get_mode(progesterone, multimodal.na = "FALSE"))
colnames(df)[which(colnames(df) == "progesterone")] <- "average progesterone"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#average vaginal_ph across visits
#Some patients have na so goes to 568
df <- aggregate(vaginal_pH ~ SUBJECT_ID, data = meta%>%data.frame, FUN = mean)
colnames(df)[which(colnames(df) == "vaginal_pH")] <- "mean_vaginal_ph"
demog <- merge(demog,df, by = c("SUBJECT_ID") )
#alpha diversity
df <- aggregate(alpha_div ~ SUBJECT_ID, data = full_set%>%data.frame, FUN = mean)
colnames(df)[which(colnames(df) == "alpha_div")] <- "mean_alpha_div"
demog <- merge(demog,df, by = c("SUBJECT_ID") )

#reported_ga is only for minimum recorded visit for each individual
Varnames <- c("mean_vaginal_ph", "number of visits", "reported_ga", "mean_alpha_div", "average income", 
              "average education", "average age", "average p_care_start", "average inf_trt", 
              "average  v_discharge", "average v_odor", "average v_itching", "average douche", 
              "average bv_history", "average vaginitis_history", "average current_medications",
              "average smoker_current", "average alcohol_frequency", "average diabetes", 
              "average no_false_labor", "average no_high_bp", "average no_vbleeding", "average progesterone")
#racial breakdown, break down visits and other demographics
stratified1 = tableone::CreateTableOne(
  vars = Varnames[1:4],
  data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
stratified2 = tableone::CreateTableOne(
  vars = Varnames[5:7],
  data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
stratified3 = tableone::CreateTableOne(
  vars = Varnames[8:15],
  data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
stratified4 = tableone::CreateTableOne(
  vars = Varnames[16:19],
  data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)
stratified5 = tableone::CreateTableOne(
  vars = Varnames[20:23],
  data = summarytools::unlabel(demog), strata = "race", includeNA = TRUE)

```

```{r }
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
#  knitr::kable(format="latex")%>% 
#  kable_styling("striped", full_width = F, font_size = 9)
stratified1
#fix the part where every race is shown, probably due to meta being called
```

```{r }
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
#  knitr::kable(format="latex")%>% 
#  kable_styling("striped", full_width = F, font_size = 9)
stratified2
#fix the part where every race is shown, probably due to meta being called
```

```{r }
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
#  knitr::kable(format="latex")%>% 
#  kable_styling("striped", full_width = F, font_size = 9)
stratified3
#fix the part where every race is shown, probably due to meta being called
```

```{r }
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
#  knitr::kable(format="latex")%>% 
#  kable_styling("striped", full_width = F, font_size = 9)
stratified4
#fix the part where every race is shown, probably due to meta being called
```

```{r }
#stratified1 <- print(stratified1, printToggle = FALSE, showAllLevels = FALSE)
#stratified1 = stratified1[,!(colnames(stratified1) %in% "test")]%>%
#  knitr::kable(format="latex")%>% 
#  kable_styling("striped", full_width = F, font_size = 9)
stratified5
#fix the part where every race is shown, probably due to meta being called
```

```{r}
#Find out individuals who have a different vagitype across visits
uniq_vagitype <- full_set %>%
  group_by(SUBJECT_ID) %>%
  summarize(n_unique = n_distinct(vagitype))
more_vagitype <- uniq_vagitype %>%
  filter(n_unique > 1)
full_set %>%
  filter(SUBJECT_ID %in% more_vagitype$SUBJECT_ID) %>%
  group_by(SUBJECT_ID) %>%
  distinct(vagitype)

#Look at proportional changes over time, look at differences betweeen those that did change and those that did not
#Look at how they change, if they change similarly
```



```{r}

count_table <- aggregate(list(full_set$Lactobacillus_iners, full_set$p_Iners,full_set$Lactobacillus_crispatus_cluster, full_set$p_Crisp, full_set$Gardnerella_vaginalis, full_set$p_Gard, full_set$Lactobacillus_gasseri_cluster, full_set$Gass, full_set$Lachnospiraceae_BVAB1, full_set$p_BVAB), by = list(full_set$race, full_set$reported_ga), mean)
colnames(count_table) <- c("race", "reported_ga", "Lactobacillus_iners", "p_Iners", "Crispatus", "p_Crisp", 
                           "Gardnerella", "p_Gard", "Gasseri", "p_Gass", "BVAB", "p_BVAB")
count_table <- count_table %>%
  arrange(race)
kable(count_table,  table.attr = "style = \"color: black;\"") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
```

```{r }
#Plot proportions over time, plot count over time
iners <- ggplot(count_table, aes(reported_ga, p_Iners)) + 
  geom_line(aes(group=race, color=race)) 
crisp <- ggplot(count_table, aes(reported_ga, p_Crisp)) + 
  geom_line(aes(group=race, color=race)) 
gard <- ggplot(count_table, aes(reported_ga, p_Gard)) + 
  geom_line(aes(group=race, color=race)) 
gass <- ggplot(count_table, aes(reported_ga, p_Gass)) + 
  geom_line(aes(group=race, color=race)) 
bvab <- ggplot(count_table, aes(reported_ga, p_BVAB)) + 
  geom_line(aes(group=race, color=race)) 
library(gridExtra)
ggarrange(iners, crisp, bvab, gard, gass, nrow = 3)
#Plot individuals for each race
#ggplot(full_set, aes(reported_ga, p_Iners)) + 
#  geom_line(aes(group=SUBJECT_ID, color=race)) + 
#  facet_wrap(~race, ncol=1)


#Look at those with the specific taxa as their vagitype
```


```{r}
prop_plot <- ggplot(full_set, aes(p_Iners)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_Iners), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_Iners), lwd=1, linetype=2, color="green")
#prop_plot
cprop_plot <- ggplot(full_set, aes(p_Crisp)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_Crisp), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_Crisp), lwd=1, linetype=2, color="green")
#iprop_plot

bprop_plot <- ggplot(full_set, aes(p_BVAB)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_BVAB), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_BVAB), lwd=1, linetype=2, color="green")
#pprop_plot

gprop_plot <- ggplot(full_set, aes(p_Gard)) + geom_histogram() + geom_vline(xintercept=mean(full_set$p_Gard), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$p_Gard), lwd=1, linetype=2, color="green")
#tprop_plot

gasprop_plot <- ggplot(full_set, aes(Gass)) + geom_histogram() + geom_vline(xintercept=mean(full_set$Gass), lwd=1, linetype=2, color="red") + geom_vline(xintercept=median(full_set$Gass), lwd=1, linetype=2, color="green")
#bprop_plot
#stratify by race
ggarrange(prop_plot, cprop_plot, bprop_plot, gprop_plot, gasprop_plot, labels = c("Iners","Crisp", "BVAB", "Gard", "Gass"), ncol = 3, nrow = 2)

```

```{r}
race_prop_plot <- ggplot(full_set, aes(p_Iners)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race) 

crace_prop_plot <- ggplot(full_set, aes(p_Crisp)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race) 

#stratify by race
brace_prop_plot <- ggplot(full_set, aes(p_BVAB)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race) 

grace_prop_plot <- ggplot(full_set, aes(p_Gard)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race)

gassrace_prop_plot <- ggplot(full_set, aes(Gass)) + geom_histogram(aes(group=race, fill=race)) + facet_wrap(~race)

ggarrange(race_prop_plot, crace_prop_plot, brace_prop_plot, grace_prop_plot, gassrace_prop_plot, nrow = 3)

```


```{r Count}
#Zero-Inflated Poisson Model
z <- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + (1|SUBJECT_ID), data = full_set, family = poisson, zi = ~ vaginal_pH + reported_ga + alpha_div)
summary(z)
hist(predict(z))

#summary(m1 <- zeroinfl(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div |SUBJECT_ID, data = full_set))
z <- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + (1|SUBJECT_ID), data = full_set, family = poisson, zi = ~ 0)
summary(z)
hist(predict(z))


#Zero-Inflated Negative Binomial Model
neg_bin<- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + offset(log(num_reads)) + (1|SUBJECT_ID), data = full_set, family = nbinom2, zi = ~ vaginal_pH + reported_ga + alpha_div)
summary(neg_bin)
predict(neg_bin)
neg_binone <- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + offset(log(num_reads)) + (1|SUBJECT_ID), data = full_set, family = nbinom2, zi = ~ 1)
summary(neg_binone)
hist(predict(neg_binone))
#Zero-Inflated Beta-Binomial Model
#kostic.y <- cbind(1, kostic.y=="Tumor")
beta_bin<- glmmTMB(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set, family=betabinomial(link = "logit"), zi = ~ vaginal_pH + reported_ga + alpha_div + race)


#Mixed Linear Regression Model
mixedL <- lmer(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set)
summary(mixedL)
linpred <- predict(mixedL)
plot(full_set$Lactobacillus_iners[which(names(linpred) %in% rownames(full_set))], linpred)
#predicted
#x-axis people
#y-axis visits for that person
plot(full_set$Lactobacillus_iners[which(names(linpred) %in% rownames(full_set))], linpred)


## Beta bimonial zero inflated ##

## NBZIMM ##
f21 = glmm.zinb(Lactobacillus_iners ~ vaginal_pH + reported_ga + alpha_div , random = ~ 1|SUBJECT_ID, data = full_set)
summary(f21)
summary(f21)$tTable[4, 5]

```

```{r ProportionsData}
hist(full_set$p_Iners)
beta_bin<- glmmTMB(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set, family=betabinomial(link = "logit"), zi = ~ vaginal_pH + reported_ga + alpha_div + race)
mean(full_set$p_Iners)
var(full_set$p_Iners)

library(zoib)
#zoib
#zoib(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race | 1|1, data = full_set, random = 1, EUID = full_set$SUBJECT_ID)
library(aod)
full_set$p_Iners[which(full_set$p_Iners == 1)] <- 0.99999
betaIners <- glmmTMB(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set, family=beta_family(link = "logit"), zi = ~ 1)
betapred <- predict(betaIners)
summary(betaIners)
#predicted
#x-axis people
#y-axis visits for that person
#plot(full_set$p_Iners[which(names(betapred) %in% rownames(full_set))], betapred)

#zoib(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race | 1, data = full_set)


mixedL <- lmer(p_Iners ~ vaginal_pH + reported_ga + alpha_div + race + (1|SUBJECT_ID), data = full_set)

#par(mfrow = c(2,2))

coefplot2(mixedL)
plot(mixedL)
linpred <- predict(mixedL)

#predicted
#x-axis people
#y-axis visits for that person
plot(full_set$p_Iners[which(names(linpred) %in% rownames(full_set))], linpred)
```


```{r}
full_set$class_Iners <- as.factor(ifelse(full_set$p_Iners > mean(full_set$p_Iners), "High", "Low"))
full_set$class_Crisp <- as.factor(ifelse(full_set$p_Crisp > mean(full_set$p_Crisp), "High", "Low"))
full_set$class_Gass <- as.factor(ifelse(full_set$Gass > mean(full_set$Gass), "High", "Low"))
full_set$class_Gard <- as.factor(ifelse(full_set$p_Gard > mean(full_set$p_Gard), "High", "Low"))
full_set$class_BVAB <- as.factor(ifelse(full_set$p_BVAB > mean(full_set$p_BVAB), "High", "Low"))


#Check median
#add line to show where each point is
```


```{r}
#install.packages("ggpubr")
iners_plot <- ggplot(full_set, aes(class_Iners)) + geom_bar()
snea_plot <- ggplot(full_set, aes(class_Crisp)) + geom_bar() 
tm_plot <- ggplot(full_set, aes(class_Gass)) + geom_bar() 
prev_plot <- ggplot(full_set, aes(class_Gard)) + geom_bar() 
bvab_plot <- ggplot(full_set, aes(class_BVAB)) + geom_bar()

ggarrange(iners_plot, snea_plot, tm_plot, prev_plot, bvab_plot, labels = c("Iners", "Crisp", "Gass", "Gard", "BVAB1"), ncol = 3, nrow = 2)


```

```{r}
#Distribution of High and Low (do it across race)
irace_prop_plot <- ggplot(full_set, aes(class_Iners)) + geom_bar(aes(y= stat(prop),group=race, fill=race), position = "dodge2")+ ggtitle("Iners Dichotomized Histogram")
irace_prop_plot

#Distribution of High and Low (do it across race)
srace_prop_plot <- ggplot(full_set, aes(class_Crisp)) + geom_bar(aes(y= stat(prop), group=race, fill=race), position = "dodge2") + ggtitle("Crisp Dichotomized Histogram")


#Distribution of High and Low (do it across race)
prace_prop_plot <- ggplot(full_set, aes(class_Gass)) + geom_bar(aes(y= stat(prop), group=race, fill=race), position = "dodge2") +  ggtitle("Gass Dichotomized Histogram")


#Distribution of High and Low (do it across race)
trace_prop_plot <- ggplot(full_set, aes(class_Gard)) + geom_bar(aes(y= stat(prop), group=race, fill=race), position = "dodge2") + ggtitle("Gard Dichotomized Histogram")


#Distribution of High and Low (do it across race)
brace_prop_plot <- ggplot(full_set, aes(class_BVAB)) + geom_bar(aes(y= stat(prop),group=race, fill=race), position = "dodge2") + ggtitle("BVAB Dichotomized Histogram")


ggarrange(irace_prop_plot, srace_prop_plot, prace_prop_plot, trace_prop_plot, brace_prop_plot, nrow = 3)
#Use proportions instead of counts for better comparison, DONE
#position dodge, DONE
#Add proportion values to top of bars
#do facet wrap by taxa
```

```{r}
covs.taxa <- function(df, taxon, model, vars){
  tax_prop <- df[,taxon]/df[,"num_reads"]
   if(model == "fixed"){
    #covs <- vars
    model_data <- df[,vars]
    half <- paste(vars, collapse= "+")
  }
  else{
    #covs <- vars
    model_data <- df[,c(vars, "SUBJECT_ID", "num_reads")]
    half <- paste(paste(vars, collapse= "+"), " + (1|SUBJECT_ID)")
  }
  model_data <- cbind(tax_prop, model_data)
  no.na.data <- na.omit(model_data)
  form <- as.formula(paste("tax_prop ~ ", half))
  if(model == "fixed"){
    mod <- lm(form, data = no.na.data)
    step.back <- stepAIC(mod, direction = "backward")
    return(step.back)
    #return(mod)
  }
  else{
    mod <- lmer(form, data = no.na.data)
    return(mod)
    #return(mod.step)
  }
}


```


```{r }
taxa.glm <- function(df, taxon, type, model){
  tax_count <- df[,taxon]
  tax_prop <- df[,taxon]/df[,"num_reads"]
  class_tax <- as.factor(ifelse(tax_prop > mean(tax_prop), "High", "Low"))
  class_tax <- recode(class_tax, "High"= 1, "Low"= 0)
  if(model == "fixed"){
    covs <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
    model_data <- df[,covs]
    half <- paste(covs, collapse= "+")
  }
  else{
    covs <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
    model_data <- df[,c(covs, "SUBJECT_ID", "num_reads")]
    half <- paste(paste(covs, collapse= "+"), " + (1|SUBJECT_ID)")
  }
  if(type == "count"){
    model_data <- cbind(tax_count, model_data)
    no.na.data <- na.omit(model_data)
    form <- as.formula(paste("tax_count ~ ", half))
    if(model == "fixed"){
      mod <- glm.nb(form, data = no.na.data)
    }
    else{
      mod <- glmer.nb(form, data = no.na.data)
    }
  }
  else if(type == "prop"){
    model_data <- cbind(tax_prop, model_data)
    no.na.data <- na.omit(model_data)
    form <- as.formula(paste("tax_prop ~ ", half))
    if(model == "fixed"){
      mod <- lm(form, data = no.na.data)
      #return(mod)
    }
    else{
      mod <- lmer(form, data = no.na.data)
      return(mod)
      #return(mod.step)
    }
  }
  else{
    model_data <- cbind(class_tax, model_data)
    no.na.data <- na.omit(model_data)
    form <- as.formula(paste("class_tax ~ ", paste(covs, collapse= "+")))
    if(model == "fixed"){
      mod <- glm(form, data = no.na.data, family = binomial)
    }
    else{
      mod <- glmer(form, data = no.na.data, family = binomial, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
    }
  }
  #step.model <- stepAIC(fixed_mod, direction = "both", trace = FALSE)
  step.back <- stepAIC(mod, direction = "backward", trace = FALSE)
  return(step.back)
  #TO ADD: Plot for each covariate the predicted distribution
  #Output table of common variables, extract p-value
}
```

```{r }
model_plot <- function(model){
  final_covs <- names(model$model)[-1]
  print(deparse(substitute(model)))
  print(as.formula(paste("class_tax ~ ", paste(final_covs, collapse= "+"))))
  #Plot AIC for model
  vars <- unlist(lapply(strsplit(as.character(model$anova$Step), '- ', fixed = TRUE), '[', 2))
  vars[1] <- "Intercept"
  AIC.res <- data.frame(Variable = vars, AIC = model$anova$AIC)
  AIC.res$Variable <- factor(AIC.res$Variable, levels = vars)
  #true variables
  #true_vars <- paste0("var", 1:10)
  #colour_vars <- ifelse(AIC.res$Variable %in% true_vars, 'red','black')
  plot <- ggplot(data=AIC.res, aes(x=Variable, y=AIC, group=1)) +
    geom_line(color = "red")+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 60, hjust = 1))
  return(plot)
}
```

```{r }
pred_plot <- function(df, model){
  model_data <- df[,covs]
  model_data <- cbind(class_tax, model_data)
  no.na.data <- na.omit(model_data)
  final_covs <- names(model$model)[-1]
  pred_data <- no.na.data[,final_covs]
  return(predict(model, newdata=pred_data, type = "response"))
  
  
  
  # if(c("alpha_div","vagitype") %in% names(model$model)){
  #   plotting_dfm <- expand.grid(alpha_div = seq(0, 3.5, 0.01),
  #                          vagitype = c("Lactobacillus_crispatus_cluster", "Lactobacillus_iners", "Lachnospiraceae_BVAB1", "Gardnerella_vaginalis", "Lactobacillus_gasseri_cluster"))
  #   plotting_dfm$preds <- predict(model, newdata=plotting_dfm, type = "response")
  #   pl <- ggplot(plotting_dfm, aes(x=alpha_div, y =preds, color=as.factor(vagitype))) +
  #     geom_point( ) +
  #     ggtitle("Predicted High/Low by Alpha_Div and Vagitype")
  #   return(p1)
  # }
  # else{
  #   return ("No value")
  # }
}

```

```{r}
get_data <- function(taxon, df){
  tax_prop <- df[,taxon]/df[,"num_reads"]
  covs <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
  model_data <- df[,c(covs, "SUBJECT_ID", "num_reads")]
  model_data <- cbind(tax_prop, model_data)
  no.na.data <- na.omit(model_data)
  return(no.na.data)
}
```

```{r}
make_hists<- function(model, taxon, df, vars){
  hists <- data.frame(xx = c(predict(model, newdata = get_data(taxon, df)[, vars]), get_data(taxon, df)[, "tax_prop"]), yy = rep(c("pred", "actual"),each = length(get_data(taxon, df)[, "tax_prop"])))
  return(ggplot(hists, aes(x=xx, fill=yy)) + geom_histogram(alpha=0.2, position="identity"))
}

```

```{r}
agg_plot <- function(model, taxon, df, vars){
  ex <- data.frame(data = c(predict(model, newdata = get_data(taxon, df)[, vars]), get_data(taxon, df)[, "tax_prop"]), 
                   race = get_data(taxon, df)[, "race"], tri = get_data(taxon, df)[, "reported_ga"],
                   yy = rep(c("pred", "actual"),each = length(get_data(taxon, df)[, "tax_prop"])))
  agg_table <- aggregate(list(ex$data), by = list(ex$race, ex$tri, ex$yy), mean)
  colnames(agg_table) <- c("race", "tri", "type", "data")
  agg_plot <- ggplot(agg_table, aes(tri, data)) + 
geom_line(aes(group=interaction(race, type), color=race, linetype = type)) + 
    scale_x_discrete(labels=c("First Trimester" = "1st", "Second Trimester" = "2nd",
                              "Third Trimester" = "3rd")) + 
    ylab("Prop")
  return(agg_plot)
}

```

```{r}
getPerformace <- function(model, df){
  taxa <- colnames(t_otu)
  example <- c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster", "Gardnerella_vaginalis", "Lachnospiraceae_BVAB1", "Lactobacillus_gasseri_cluster", "Sneathia_amnii", "Prevotella_cluster2", "TM7_OTU-H1")
  for (name in example){
    md <- covs.taxa
  }
  perf <- model_performance(model, metrics = "all", verbose = TRUE)
  return(perf)
  
}


```

```{r}
subsetFull <- function(df, taxalist){
  `%ni%` <- Negate(`%in%`)
  cols <- sapply(taxalist, function (x) any(df[,x]/df$num_reads > 0.05))
  df <- subset(df,select = names(df) %ni% names(which(cols == FALSE)))
  return(df)
}
  

```

```{r}
covs <- c("vaginal_pH", "alpha_div", "reported_ga", "race", "income", "bv_history", "phys", "antibiotics_current","age", "education", "prenatal_care_start", "infertility_treatment", "vdischarge", "vodor", "vitching", "douche", "vaginitis_history", "current_medications", "smoker_current", "alcohol_frequency_year", "diabetes", "no_false_labor", "no_high_bp", "no_vbleeding", "progesterone")
model_data <- full_set[,covs]
ggpairs(model_data, columns = 1:2, ggplot2::aes(colour=race)) #too many points, too many vagitypes as well so had to remove
#do top 5 vagitypes
aa <- full_set[full_set$race == 'african_american',]
histogram(aa$vaginal_pH)
```


```{r CompareModelType}
count_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "count", "fixed") #Does not converge
prop_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "prop", "fixed")
class_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "class", "fixed")
mcount_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "count", "mixed") #Does not converge
mprop_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "prop", "mixed") #failure to converge in 10000 evaluations
mod_Crisp <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "class", "fixed")
mod_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "class", "fixed")
mod_BVAB <- taxa.glm(full_set, "Lachnospiraceae_BVAB1", "class", "fixed")
mod_Gard <- taxa.glm(full_set, "Gardnerella_vaginalis", "class", "fixed")
mod_Gass <- taxa.glm(full_set, "Lactobacillus_gasseri_cluster", "class", "fixed")


taxalist <- c("Lactobacillus_crispatus_cluster", "Lactobacillus_iners", "Lachnospiraceae_BVAB1", "Gardnerella_vaginalis", "Lactobacillus_gasseri_cluster")
var_df <- data.frame(matrix(ncol = 15, nrow = 15))
#colnames(var_df) <- taxalist
count <- 0
for(ind in c(1:length(taxalist))){
  mods <- taxa.glm(full_set, taxalist[ind], "prop", "mixed")
  no.na.data <- get_data(taxalist[ind], full_set)
  step.back <- lmerTest::step(mods)
  fix_vars <- c(rownames(step.back$fixed[which(step.back$fixed$Eliminated == 0),]), rownames(step.back$random[which(step.back$random$Eliminated == 0),]))
  for(i in c(1:length(fix_vars))){
    var_df[i,ind+count] <- fix_vars[i]
  }
  fixed_mods <- taxa.glm(full_set, taxalist[ind], "prop", "fixed")
  fixed_vars <- names(fixed_mods$model)[-1]
  for(i in c(1:length(fixed_vars))){
    var_df[i,ind+1+count] <- fixed_vars[i]
  }
  class_mods <- taxa.glm(full_set, taxalist[ind], "class", "fixed")
  class_vars <- names(class_mods$model)[-1]
  for(i in c(1:length(class_vars))){
    var_df[i,ind+2+count] <- class_vars[i]
  }
  count <- count + 2
}

colnames(var_df) <- c("Crisp_P_M", "Crisp_P_F", "Crisp_C_F", "Iners_P_M", "Iners_P_F","Iners_C_F", "BVAB1_P_M", "BVAB1_P_F","BVAB1_C_F","Gard_P_M", "Gard_P_F", "Gard_C_F","Gass_P_M","Gass_P_F", "Gass_C_F")
options(knitr.kable.NA = '')
kable(var_df, table.attr = "style = \"color: black;\"") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  add_header_above(c("Crispatus" = 3, "Iners" = 3, "BVAB" = 3, "Gardnerella" = 3, "Gasseri" = 3))

```

```{r}
#Make table for the variables that are outputted
#need the data frame
#alpha div, vagitype, age, smoking, education, subject_id, vaginal_ph, race
#look at covariate estimates for each taxa model, consider correlations in model
#forward model selection, one variable addition at a time
#look at differences between taxa
#look for vagitypes in rare taxa
#performance with and without vagitype, compare using model comparison,ANOVA DONE
#overlay averages across each race across the 3 trimesters, output model metrics, try without and with vagitype: WROTE FUNCTION, APPLY TO ALL TAXA
#how much does vagitype add in terms of prediction, PLOTS DONE
#make summaries
#pca under both smoothed and unsmoother conditions
#prcomp on raw data, scale variables to same unit

vars <- c("vaginal_pH", "alpha_div", "race", "vagitype", "age", "education", "smoker_current")
mx_vars<- c("vaginal_pH", "alpha_div", "race", "vagitype", "age", "education", "smoker_current", "reported_ga","SUBJECT_ID")
mx_type <- c("vaginal_pH", "alpha_div", "race", "age", "education", "smoker_current", "reported_ga","SUBJECT_ID")
no_type <- c("vaginal_pH", "alpha_div", "race", "age", "education", "smoker_current")

#predict based on these models

taxalist <- c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster", "Gardnerella_vaginalis", "Lachnospiraceae_BVAB1", "Lactobacillus_gasseri_cluster", "Sneathia_amnii", "Prevotella_cluster2", "TM7_OTU-H1")
tax_plots <- list()
tax_hists <- list()

#taxalist <- c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster")
for (taxa in taxalist){
 mixed_full <- covs.taxa(full_set, taxa, "mixed", vars)
 mixed_notype <- covs.taxa(full_set, taxa, "mixed", no_type)
 print(taxa)
 print(anova(mixed_full, mixed_notype))
 #print(summary(mixed_notype))
 var = paste(taxa, "Full", sep = "_")
 var2 = paste(taxa, "NoType", sep = "_")
 tax_plots[[var]] <- agg_plot(mixed_full, taxa, full_set, mx_vars) + ggtitle(var)
 tax_plots[[var2]] <- agg_plot(mixed_notype, taxa, full_set, mx_type) + ggtitle(var2)
 tax_hists[[var]] <- make_hists(mixed_full, taxa, full_set, mx_vars) + ggtitle(var)
 tax_hists[[var2]] <- make_hists(mixed_notype, taxa, full_set, mx_type) + ggtitle(var2)
}
new_list = c("Lactobacillus_iners", "Lactobacillus_crispatus_cluster", "Gardnerella_vaginalis", "Lachnospiraceae_BVAB1", "Lactobacillus_gasseri_cluster")
perf <- data.frame()
predFrame <- data.frame()
see <- subsetFull(full_set, names(t_otu))
updated <- t_otu[,(names(t_otu) %in% names(see))]

#comb <- data.frame()
for (taxa in names(updated)){
  mixed_full <- covs.taxa(see, taxa, "mixed", vars)
  if (taxa %in% new_list){
    orig <- paste(taxa, "Orig", sep = "_")
    predicted <- paste(taxa, "Pred", sep = "_")
    pre_dat <- getData(mixed_full)
    pred <- predict(mixed_full)
    comb[1:length(pred),c(orig, predicted)] <- cbind(pre_dat[,c("tax_prop", "SUBJECT_ID")], pred)
    # comb <- comb %>%
    #   rename(!!orig := tax_prop, !!predicted := pred)
  }
  var = paste(taxa, "Full", sep = "_")
  predFrame[1:length(predict(mixed_full)),taxa] <- predict(mixed_full)
  perf[taxa,names(model_performance(mixed_full, metrics = "all", verbose = TRUE))] <- model_performance(mixed_full, metrics = "all", verbose = TRUE)
}
predFrame[predFrame < 0] <- 0
predFrame[predFrame > 1] <- 1
comb[comb < 0] <- 0
comb[comb > 1] <- 1
comb <- comb[ , c(2, 1, 3:ncol(comb))]
no_na <- na.omit(full_set[, vars])
pred_Full <- cbind(predFrame, no_na)
#Get the actual from the initial model
#Add in subject_id
#Truncate, DONE
```

```{r}
perf$rankRMSE <- rank(perf$RMSE)
perf$rankR2Cond <- rank(-perf$R2_conditional)
perf$Name <- rownames(perf)
kable(perf,  table.attr = "style = \"color: black;\"") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
#make boxplot for the metrics and look at outliers, use rmse
#see rank of metrics for specific taxa of interest
#add column for quantiles for the specific taxa of interest

rmse <- ggplot(perf, aes(x="RMSE", y=RMSE)) + 
  geom_boxplot() +
  geom_point(data=subset(perf, rownames(perf) %in% new_list), 
    aes(x="RMSE", y=RMSE), 
    color="red", size=5) +
  geom_text_repel(data=subset(perf, rownames(perf) %in% new_list), aes(label=Name))
rmse

r2_cond <- ggplot(perf, aes(x="R2_Cond", y=R2_conditional)) + 
  geom_boxplot() +
  geom_point(data=subset(perf, rownames(perf) %in% new_list), 
    aes(x="R2_Cond", y=R2_conditional), 
    color="red", size=5) +
  geom_text_repel(data=subset(perf, rownames(perf) %in% new_list), aes(label=Name))
r2_cond
```

```{r PredActual}
grouped <- aggregate(. ~ SUBJECT_ID, comb, mean)
grouped$rows <- as.numeric(rownames(grouped))

sorted <- comb %>%
  group_by(SUBJECT_ID) %>%
  filter(n() > 7)
sorted <- sorted %>%
  group_by(SUBJECT_ID) %>%
  mutate(id = row_number())
sorted <- ungroup(sorted)


ins = ggplot() + 
  geom_line(data = sorted, aes(x=id, y=Lactobacillus_iners_Orig,group=as.factor(SUBJECT_ID)), color = "blue") +
  geom_line(data = sorted, aes(x=id, y=Lactobacillus_iners_Pred,group=as.factor(SUBJECT_ID)), color = "red") +
  xlab('Number of Visits') +
  ylab('Prop_Iners') +
  ggtitle("Lactobacillus iners over time")
ins <- ggplotly(ins)
ins

crp = ggplot() + 
  geom_line(data = sorted, aes(x=id, y=Lactobacillus_crispatus_cluster_Orig,group=SUBJECT_ID), color = "blue") +
  geom_line(data = sorted, aes(x=id, y=Lactobacillus_crispatus_cluster_Pred,group=SUBJECT_ID), color = "red") +
  xlab('Number of Visits') +
  ylab('Prop_Crisp') +
  ggtitle("Lactobacillus crispatus over time")
crp <- ggplotly(crp)
crp

grd = ggplot() + 
  geom_line(data = sorted, aes(x=id, y=Gardnerella_vaginalis_Orig,group=SUBJECT_ID), color = "blue") +
  geom_line(data = sorted, aes(x=id, y=Gardnerella_vaginalis_Pred,group=SUBJECT_ID), color = "red") +
  xlab('Number of Visits') +
  ylab('Prop_Gard') +
  ggtitle("Gardnerella vaginalis over time")
grd <- ggplotly(grd)
grd

bvb = ggplot() + 
  geom_line(data = sorted, aes(x=id, y=Lachnospiraceae_BVAB1_Orig,group=SUBJECT_ID), color = "blue") +
  geom_line(data = sorted, aes(x=id, y=Lachnospiraceae_BVAB1_Pred,group=SUBJECT_ID), color = "red") +
  xlab('Number of Visits') +
  ylab('Prop_BVAB1') +
  ggtitle("BVAB1 over time")
bvb <- ggplotly(bvb)
bvb

gas = ggplot() + 
  geom_line(data = sorted, aes(x=id, y=Lactobacillus_gasseri_cluster_Orig,group=SUBJECT_ID), color = "blue") +
  geom_line(data = sorted, aes(x=id, y=Lactobacillus_gasseri_cluster_Pred,group=SUBJECT_ID), color = "red") +
  xlab('Number of Visits') +
  ylab('Prop_Gass') +
  ggtitle("Lactobacillus gasseri over time") +
  guides(fill=guide_legend(title="Orig/Pred"))
gas <- ggplotly(gas)
gas



#How to best effectively plot all taxa for predicted vs actual without having too many graphs
#sindex
# ggplot(aes(x = hp, y = mpg)) +
#   geom_smooth(method = "lm", se = FALSE, color = "lightgrey") +
#   geom_segment(aes(xend = hp, yend = predicted), alpha = .2) +
# 
#   # > Color adjustments made here...
#   geom_point(aes(color = residuals)) +  # Color mapped here
#   scale_color_gradient2(low = "blue", mid = "white", high = "red") +  # Colors to use here
#   guides(color = FALSE) +
#   # <
# 
#   geom_point(aes(y = predicted), shape = 1) +
#   theme_bw()

#Do high, medium, low for residuals
#Legend
#Add each subject id, color by id, line type by predicted/acutal
```

```{r TrimesterPlots}
marrangeGrob(tax_plots, nrow = 2, ncol = 2)
```

```{r Histograms}
marrangeGrob(tax_hists, nrow = 2, ncol = 2)
```

```{r}
#add predicted vs actual plot, y = x
#put table for prediction summary/statistics, See if should add more, 
#drop all taxa that have proportion of 0.05 or below, DONE
#which(apply(t_otu, 2, sum) == 0)
pca_orig <- prcomp(updated, scale = TRUE)
coords_orig <- pca_orig$x[,1:2]
#pca_other <- prcomp(otu, scale = TRUE)
pca_pred <- prcomp(predFrame, scale = TRUE)
coords_pred <- pca_pred$x[,1:2]
rot_pre <- pca_pred$rotation[,1:2]
#pca_predT <- prcomp(t(predFrame), scale = TRUE)
#print(pca_orig)
#scriplot, DONE
#summary(pca_orig)
ggarrange(autoplot(pca_orig, data = see, colour = "race"),autoplot(pca_pred, data = pred_Full, colour = "race"),
          labels = c("Orig", "Pred"))
screeplot(pca_orig)
screeplot(pca_pred)

fviz_pca_var(pca_orig,
             col.var = "contrib", # Color by contributions to the PC
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
             )
fviz_pca_var(pca_pred,
             col.var = "contrib", # Color by contributions to the PC
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
             )
#plot pca for variables and variable loadings, extract subject scores, get coordinates of each point
#How to limit number of variable loadings to top 10?
#Overlay on first loading
#Plot x y for rotational
```


```{r}
autoplot(pca_pred, data = pred_Full, colour = "race")
screeplot(pca_pred)
```
```{r}
iners_f_hist <- make_hists(iners_fixed, "Lactobacillus_iners", full_set, vars) + ggtitle("Iners Fixed Plot")
iners_m_hist <- make_hists(iners_mixed, "Lactobacillus_iners", full_set, mx_vars) + ggtitle("Iners Mixed Plot")
ggarrange(iners_f_hist, iners_m_hist)

```

```{r}
crisp_f_hist <- make_hists(crisp_fixed, "Lactobacillus_crispatus_cluster", full_set, vars) + ggtitle("Crisp Fixed Plot")
crisp_m_hist <- make_hists(crisp_mixed, "Lactobacillus_crispatus_cluster", full_set, mx_vars) + ggtitle("Crisp Mixed Plot")
ggarrange(crisp_f_hist, crisp_m_hist)

#clean age, set blanks to NA, clean values, make 18-28 reference group
# do individual plots
# quantify prediction success
# get model summaries
# perform on more taxa
#education, high_school_ged is baseline, put in order of ascending degree

```

```{r }
#predictions <- pred_plot(full_set, mod_Iners)
#lapply(models, model_plot)

mod_Crisp <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "class", "fixed")
mod_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "class", "fixed")
mod_BVAB <- taxa.glm(full_set, "Lachnospiraceae_BVAB1", "class", "fixed")
mod_Gard <- taxa.glm(full_set, "Gardnerella_vaginalis", "class", "fixed")
mod_Gass <- taxa.glm(full_set, "Lactobacillus_gasseri_cluster", "class", "fixed")

prop_Crisp <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "prop", "fixed")
prop_Iners <- taxa.glm(full_set, "Lactobacillus_iners", "prop", "fixed")
prop_BVAB <- taxa.glm(full_set, "Lachnospiraceae_BVAB1", "prop", "fixed")
prop_Gard <- taxa.glm(full_set, "Gardnerella_vaginalis", "prop", "fixed")
prop_Gass <- taxa.glm(full_set, "Lactobacillus_gasseri_cluster", "prop", "fixed")

ggarrange(model_plot(mod_Crisp), model_plot(prop_Crisp),labels = c("Class Crisp", "Prop Crisp"))
ggarrange(model_plot(mod_Iners), model_plot(prop_Iners),labels = c("Class Iners", "Prop Iners"))
ggarrange(model_plot(mod_BVAB), model_plot(prop_BVAB),labels = c("Class BVAB", "Prop BVAB"))
ggarrange(model_plot(mod_Crisp), model_plot(prop_Iners),labels = c("Class Gard", "Prop Gard"))
ggarrange(model_plot(mod_Gass), model_plot(prop_Gass),labels = c("Class Gass", "Prop Gass"))
model_plot(mod_Crisp) + ggtitle("Class Crispatus")
model_plot(mod_Iners) + ggtitle("Class Iners")
model_plot(mod_BVAB) + ggtitle("Class BVAB")
model_plot(mod_Gard) + ggtitle("Class Gardnerella")
model_plot(mod_Gass) + ggtitle("Class Gasseri")

#alpha_div and vagitype show up in almost every model vaginal_pH in most of them
#correlations between data
#longitudinal plots when stratifying by covariates
#count and proportion plots
#Look at model pre and post aic
#ggaly and regular correlation matrix
#Look at distribution of NA's in covariate (pre-filtering) and see if it affects model by removing it
```


```{r }
mixed_Iners <- taxa.glm(full_set, "Lactobacillus_crispatus_cluster", "mean", "mixed")
#Some linear combination of the parameters perfectly separates failure from successes, I assume it's vagitype
#Doesn't run still

```


```{r}
na.test <-  function (x) {
  w <- sapply(x, function(x)all(is.na(x)))
  if (any(w)) {
    stop(paste("All NA in columns", paste(which(w), collapse=", ")))
  }
}
#Recode High to 1 and Low to 0
full_set[["class_Iners"]] <- recode(full_set[["class_Iners"]], "High"= 1, "Low"= 0)
full_set$class_Iners <- as.factor(full_set$class_Iners)


covs <- c("class_Iners", "vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "bv", "phys", "antibiotics_current","SUBJECT_ID")
model_data <- full_set[,covs]
no.na.data <- na.omit(model_data)

reduced_mod <- glmer(class_Iners ~ alpha_div + vagitype + (1|SUBJECT_ID), data = no.na.data, family = binomial, control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5))) #Unable to evaluate scaled gradient, model fails to converge
step.model <- stepAIC(log_mod, direction = "both", 
                      trace = FALSE)
#install.packages("ResourceSelection")
library(ResourceSelection)

fixed_mod <- glm(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + phys + bv + antibiotics_current, data = no.na.data, family = binomial) 
step.model <- stepAIC(fixed_mod, direction = "both", 
                      trace = FALSE)
step.back <- stepAIC(fixed_mod, direction = "backward")
hoslem.test(no.na.data$class_Iners, fitted(fixed_mod))
hoslem.test(no.na.data$class_Iners, fitted(step.model))
# model becomes class_Iners ~ alpha_div + vagitype
predict(fixed_mod, type = "response")
histogram(predict(fixed_mod, type = "response"))
plot(predict(fixed_mod, type = "response"), full_set$class_Iners)



plotting_dfm <- expand.grid(alpha_div = seq(0, 3.5, 0.01),
                           vagitype = c("Lactobacillus_crispatus_cluster", "Lactobacillus_iners", "Lachnospiraceae_BVAB1", "Gardnerella_vaginalis", "Lactobacillus_gasseri_cluster"))
plotting_dfm$preds <- predict(step.model, newdata=plotting_dfm, type = "response")
pl <- ggplot(plotting_dfm, aes(x=alpha_div, y =preds, color=as.factor(vagitype)))
pl + 
  geom_point( ) +
  ggtitle("Predicted High/Low by Alpha_Div and Vagitype")

#Look at alpha div and vagitype over trimesters and see how it changes 
# plot those who change
#Table of amt that change vs not change
```

```{r }
reduced_mod <- glmer(class_Crisp ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
reduced_mod <- glmer(class_Gass ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
reduced_mod <- glmer(class_Gard ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
reduced_mod <- glmer(class_BVAB ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + (1|SUBJECT_ID), data = full_set, family = binomial) #Does not converge
```

```{r}
library(caret)

#Split Data 80:20
training.samps <- full_set$class_Iners %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + SUBJECT_ID, data = full_set)
#summary(mda_model)
mda_model$percent.explained
mda_model$confusion
covs <- covs[which(names(pred) %in% rownames(full_set)),]
#mda_pred <- predict(mda_model,test.data)


# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "income", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + income + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_Iners[which(names(pred) %in% rownames(full_set))])
#Random Forest
#try randomforest with formula and see if random var works
#install.packages("randomForest")
#library(randomForest)
#full_set$vagitype <- as.character(full_set$vagitype)
#full_set$SUBJECT_ID <- as.character(full_set$SUBJECT_ID)
#forest_model <- randomForest(class_Iners ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, na.action=na.omit, importance = TRUE)

```

```{r Snea}

#Split Data 80:20
training.samps <- full_set$class_Snea %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_Snea ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
mda_model$confusion
covs <- covs[which(names(pred) %in% rownames(full_set)),]
#mda_pred <- predict(mda_model,test.data)


# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_Snea ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_Snea[which(names(pred) %in% rownames(full_set))])
```


```{r Prev}

#Split Data 80:20
training.samps <- full_set$class_Prev %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_Prev ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
covs <- covs[which(names(pred) %in% rownames(full_set)),]
mda_model$confusion

#mda_pred <- predict(mda_model,test.data)


# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_Prev ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_Prev[which(names(pred) %in% rownames(full_set))])
```

```{r TM7}

#Split Data 80:20
training.samps <- full_set$class_TM %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_TM ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
covs <- covs[which(names(pred) %in% rownames(full_set)),]
mda_model$confusion
#mda_pred <- predict(mda_model,test.data)


# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_TM ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_TM[which(names(pred) %in% rownames(full_set))])
```

```{r BVAB}

#Split Data 80:20
training.samps <- full_set$class_BVAB %>%
  createDataPartition(p = 0.8, list = FALSE)
train.data <- full_set[training.samps,]
test.data <- full_set[-training.samps,]
#Linear discriminant analysis
#install.packages("mda")
library(mda)
covs_test <- as.data.frame(test.data[,cov])
full_set$vagitype <- as.factor(full_set$vagitype)
full_set$SUBJECT_ID <- as.factor(full_set$SUBJECT_ID)
mda_model <- mda(class_BVAB ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = train.data)
#summary(mda_model)
mda_model$percent.explained
covs <- covs[which(names(pred) %in% rownames(full_set)),]
mda_model$confusion
#mda_pred <- predict(mda_model,test.data)


# SVM
library(e1071)
rownames(full_set) <- rownames(meta)
cov <- c("vaginal_pH", "reported_ga", "alpha_div", "race", "vagitype", "SUBJECT_ID")
covs <- as.data.frame(full_set[,cov])
svm_model <- svm(class_BVAB ~ vaginal_pH + reported_ga + alpha_div + race + vagitype + SUBJECT_ID, data = full_set, type = "C")
pred <- predict(svm_model, covs)
table(pred, full_set$class_BVAB[which(names(pred) %in% rownames(full_set))])
```

